To see the other types of publications on this topic, follow the link: Supervised and unsupervised learning.
Dissertations / Theses on the topic 'Supervised and unsupervised learning'
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Consult the top 50 dissertations / theses for your research on the topic 'Supervised and unsupervised learning.'
Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.
Browse dissertations / theses on a wide variety of disciplines and organise your bibliography correctly.
1
Tsang, Wai-Hung. "Kernel methods in supervised and unsupervised learning /." View Abstract or Full-Text, 2003. http://library.ust.hk/cgi/db/thesis.pl?COMP%202003%20TSANG.
Full textAbstract:
Thesis (M. Phil.)--Hong Kong University of Science and Technology, 2003.<br>Includes bibliographical references (leaves 46-49). Also available in electronic version. Access restricted to campus users.
2
Aversano, Gianmarco. "Development of physics-based reduced-order models for reacting flow applications." Thesis, Université Paris-Saclay (ComUE), 2019. http://www.theses.fr/2019SACLC095/document.
Full textAbstract:
L’objectif final étant de développer des modèles d’ordre réduit pour les applications de combustion, des techniques d’apprentissage automatique non supervisées et supervisées ont été testées et combinées dans les travaux de la présente thèse pour l’extraction de caractéristiques et la construction de modèles d’ordre réduit. Ainsi, l’application de techniques pilotées par les données pour la détection des caractéristiques d’ensembles de données de combustion turbulente (simulation numérique directe) a été étudiée sur deux flammes H2 / CO: une évolution spatiale (DNS1) et une jet à évolution temporelle (DNS2). Des méthodes telles que l’analyse en composantes principales (ACP), l’analyse en composantes principales locales (LPCA), la factorisation matricielle non négative (NMF) et les autoencodeurs ont été explorées à cette fin. Il a été démontré que divers facteurs pouvaient affecter les performances de ces méthodes, tels que les critères utilisés pour le centrage et la mise à l’échelle des données d’origine ou le choix du nombre de dimensions dans les approximations de rang inférieur. Un ensemble de lignes directrices a été présenté qui peut aider le processus d’identification de caractéristiques physiques significatives à partir de données de flux réactifs turbulents. Des méthodes de compression de données telles que l’analyse en composantes principales (ACP) et les variations ont été combinées à des méthodes d’interpolation telles que le krigeage, pour la construction de modèles ordonnées à prix réduits et calculables pour la prédiction de l’état d’un système de combustion dans des conditions de fonctionnement inconnues ou des combinaisons de modèles valeurs de paramètre d’entrée. La méthodologie a d’abord été testée pour la prévision des flammes 1D avec un nombre croissant de paramètres d’entrée (rapport d’équivalence, composition du carburant et température d’entrée), avec des variantes de l’approche PCA classique, à savoir PCA contrainte et PCA locale, appliquée aux cas de combustion la première fois en combinaison avec une technique d’interpolation. Les résultats positifs de l’étude ont conduit à l’application de la méthodologie proposée aux flammes 2D avec deux paramètres d’entrée, à savoir la composition du combustible et la vitesse d’entrée, qui ont donné des résultats satisfaisants. Des alternatives aux méthodes non supervisées et supervisées choisies ont également été testées sur les mêmes données 2D. L’utilisation de la factorisation matricielle non négative (FNM) pour l’approximation de bas rang a été étudiée en raison de la capacité de la méthode à représenter des données à valeur positive, ce qui permet de ne pas enfreindre des lois physiques importantes telles que la positivité des fractions de masse d’espèces chimiques et comparée à la PCA. Comme méthodes supervisées alternatives, la combinaison de l’expansion du chaos polynomial (PCE) et du Kriging et l’utilisation de réseaux de neurones artificiels (RNA) ont été testées. Les résultats des travaux susmentionnés ont ouvert la voie au développement d’un jumeau numérique d’un four à combustion à partir d’un ensemble de simulations 3D. La combinaison de PCA et de Kriging a également été utilisée dans le contexte de la quantification de l’incertitude (UQ), en particulier dans le cadre de collaboration de données lié (B2B-DC), qui a conduit à l’introduction de la procédure B2B-DC à commande réduite. Comme pour la première fois, le centre de distribution B2B a été développé en termes de variables latentes et non en termes de variables physiques originales<br>With the final objective being to developreduced-order models for combustion applications,unsupervised and supervised machine learningtechniques were tested and combined in the workof the present Thesis for feature extraction and theconstruction of reduced-order models. Thus, the applicationof data-driven techniques for the detection offeatures from turbulent combustion data sets (directnumerical simulation) was investigated on two H2/COflames: a spatially-evolving (DNS1) and a temporallyevolvingjet (DNS2). Methods such as Principal ComponentAnalysis (PCA), Local Principal ComponentAnalysis (LPCA), Non-negative Matrix Factorization(NMF) and Autoencoders were explored for this purpose.It was shown that various factors could affectthe performance of these methods, such as the criteriaemployed for the centering and the scaling of theoriginal data or the choice of the number of dimensionsin the low-rank approximations. A set of guidelineswas presented that can aid the process ofidentifying meaningful physical features from turbulentreactive flows data. Data compression methods suchas Principal Component Analysis (PCA) and variationswere combined with interpolation methods suchas Kriging, for the construction of computationally affordablereduced-order models for the prediction ofthe state of a combustion system for unseen operatingconditions or combinations of model input parametervalues. The methodology was first tested forthe prediction of 1D flames with an increasing numberof input parameters (equivalence ratio, fuel compositionand inlet temperature), with variations of the classicPCA approach, namely constrained PCA and localPCA, being applied to combustion cases for the firsttime in combination with an interpolation technique.The positive outcome of the study led to the applicationof the proposed methodology to 2D flames withtwo input parameters, namely fuel composition andinlet velocity, which produced satisfactory results. Alternativesto the chosen unsupervised and supervisedmethods were also tested on the same 2D data.The use of non-negative matrix factorization (NMF) forlow-rank approximation was investigated because ofthe ability of the method to represent positive-valueddata, which helps the non-violation of important physicallaws such as positivity of chemical species massfractions, and compared to PCA. As alternative supervisedmethods, the combination of polynomial chaosexpansion (PCE) and Kriging and the use of artificialneural networks (ANNs) were tested. Results from thementioned work paved the way for the developmentof a digital twin of a combustion furnace from a setof 3D simulations. The combination of PCA and Krigingwas also employed in the context of uncertaintyquantification (UQ), specifically in the bound-to-bounddata collaboration framework (B2B-DC), which led tothe introduction of the reduced-order B2B-DC procedureas for the first time the B2B-DC was developedin terms of latent variables and not in terms of originalphysical variables
3
Hasenjäger, Martina. "Active data selection in supervised and unsupervised learning." [S.l. : s.n.], 2000. http://deposit.ddb.de/cgi-bin/dokserv?idn=960209220.
Full text
4
Mansinghka, Vikash Kumar. "Nonparametric Bayesian methods for supervised and unsupervised learning." Thesis, Massachusetts Institute of Technology, 2009. http://hdl.handle.net/1721.1/53172.
Full textAbstract:
Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2009.<br>Includes bibliographical references (leaves 44-45).<br>I introduce two nonparametric Bayesian methods for solving problems of supervised and unsupervised learning. The first method simultaneously learns causal networks and causal theories from data. For example, given synthetic co-occurrence data from a simple causal model for the medical domain, it can learn relationships like "having a flu causes coughing", while also learning that observable quantities can be usefully grouped into categories like diseases and symptoms, and that diseases tend to cause symptoms, not the other way around. The second method is an online algorithm for learning a prototype-based model for categorial concepts, and can be used to solve problems of multiclass classification with missing features. I apply it to problems of categorizing newsgroup posts and recognizing handwritten digits. These approaches were inspired by a striking capacity of human learning, which should also be a desideratum for any intelligent system: the ability to learn certain kinds of "simple" or "natural" structures very quickly, while still being able to learn arbitrary -- and arbitrarily complex - structures given enough data. In each case, I show how nonparametric Bayesian modeling and inference based on stochastic simulation give us some of the tools we need to achieve this goal.<br>by Vikash Kumar Mansinghka.<br>M.Eng.
5
Sîrbu, Adela-Maria. "Dynamic machine learning for supervised and unsupervised classification." Thesis, Rouen, INSA, 2016. http://www.theses.fr/2016ISAM0002/document.
Full textAbstract:
La direction de recherche que nous abordons dans la thèse est l'application des modèles dynamiques d'apprentissage automatique pour résoudre les problèmes de classification supervisée et non supervisée. Les problèmes particuliers que nous avons décidé d'aborder dans la thèse sont la reconnaissance des piétons (un problème de classification supervisée) et le groupement des données d'expression génétique (un problème de classification non supervisée). Les problèmes abordés sont représentatifs pour les deux principaux types de classification et sont très difficiles, ayant une grande importance dans la vie réelle. La première direction de recherche que nous abordons dans le domaine de la classification non supervisée dynamique est le problème de la classification dynamique des données d'expression génétique. L'expression génétique représente le processus par lequel l'information d'un gène est convertie en produits de gènes fonctionnels : des protéines ou des ARN ayant différents rôles dans la vie d'une cellule. La technologie des micro-réseaux moderne est aujourd'hui utilisée pour détecter expérimentalement les niveaux d'expression de milliers de gènes, dans des conditions différentes et au fil du temps. Une fois que les données d'expression génétique ont été recueillies, l'étape suivante consiste à analyser et à extraire des informations biologiques utiles. L'un des algorithmes les plus populaires traitant de l'analyse des données d'expression génétique est le groupement, qui consiste à diviser un certain ensemble en groupes, où les composants de chaque groupe sont semblables les uns aux autres données. Dans le cas des ensembles de données d'expression génique, chaque gène est représenté par ses valeurs d'expression (caractéristiques), à des points distincts dans le temps, dans les conditions contrôlées. Le processus de regroupement des gènes est à la base des études génomiques qui visent à analyser les fonctions des gènes car il est supposé que les gènes qui sont similaires dans leurs niveaux d'expression sont également relativement similaires en termes de fonction biologique. Le problème que nous abordons dans le sens de la recherche de classification non supervisée dynamique est le regroupement dynamique des données d'expression génique. Dans notre cas, la dynamique à long terme indique que l'ensemble de données ne sont pas statiques, mais elle est sujette à changement. Pourtant, par opposition aux approches progressives de la littérature, où l'ensemble de données est enrichie avec de nouveaux gènes (instances) au cours du processus de regroupement, nos approches abordent les cas lorsque de nouvelles fonctionnalités (niveaux d'expression pour de nouveaux points dans le temps) sont ajoutés à la gènes déjà existants dans l'ensemble de données. À notre connaissance, il n'y a pas d'approches dans la littérature qui traitent le problème de la classification dynamique des données d'expression génétique, définis comme ci-dessus. Dans ce contexte, nous avons introduit trois algorithmes de groupement dynamiques que sont capables de gérer de nouveaux niveaux d'expression génique collectés, en partant d'une partition obtenue précédente, sans la nécessité de ré-exécuter l'algorithme à partir de zéro. L'évaluation expérimentale montre que notre méthode est plus rapide et plus précis que l'application de l'algorithme de classification à partir de zéro sur la fonctionnalité étendue ensemble de données<br>The research direction we are focusing on in the thesis is applying dynamic machine learning models to salve supervised and unsupervised classification problems. We are living in a dynamic environment, where data is continuously changing and the need to obtain a fast and accurate solution to our problems has become a real necessity. The particular problems that we have decided te approach in the thesis are pedestrian recognition (a supervised classification problem) and clustering of gene expression data (an unsupervised classification. problem). The approached problems are representative for the two main types of classification and are very challenging, having a great importance in real life.The first research direction that we approach in the field of dynamic unsupervised classification is the problem of dynamic clustering of gene expression data. Gene expression represents the process by which the information from a gene is converted into functional gene products: proteins or RNA having different roles in the life of a cell. Modern microarray technology is nowadays used to experimentally detect the levels of expressions of thousand of genes, across different conditions and over time. Once the gene expression data has been gathered, the next step is to analyze it and extract useful biological information. One of the most popular algorithms dealing with the analysis of gene expression data is clustering, which involves partitioning a certain data set in groups, where the components of each group are similar to each other. In the case of gene expression data sets, each gene is represented by its expression values (features), at distinct points in time, under the monitored conditions. The process of gene clustering is at the foundation of genomic studies that aim to analyze the functions of genes because it is assumed that genes that are similar in their expression levels are also relatively similar in terms of biological function.The problem that we address within the dynamic unsupervised classification research direction is the dynamic clustering of gene expression data. In our case, the term dynamic indicates that the data set is not static, but it is subject to change. Still, as opposed to the incremental approaches from the literature, where the data set is enriched with new genes (instances) during the clustering process, our approaches tackle the cases when new features (expression levels for new points in time) are added to the genes already existing in the data set. To our best knowledge, there are no approaches in the literature that deal with the problem of dynamic clustering of gene expression data, defined as above. In this context we introduced three dynamic clustering algorithms which are able to handle new collected gene expression levels, by starting from a previous obtained partition, without the need to re-run the algorithm from scratch. Experimental evaluation shows that our method is faster and more accurate than applying the clustering algorithm from scratch on the feature extended data set
6
Bass, Gideon. "Ensemble supervised and unsupervised learning with Kepler variable stars." Thesis, George Mason University, 2016. http://pqdtopen.proquest.com/#viewpdf?dispub=10027479.
Full textAbstract:
<p>Variable star analysis and classification is an important task in the understanding of stellar features and processes. While historically classifications have been done manually by highly skilled experts, the recent and rapid expansion in the quantity and quality of data has demanded new techniques, most notably automatic classification through supervised machine learning. I present a study on variable stars in the Kepler field using these techniques, and the novel work of unsupervised learning. I use new methods of characterization and multiple independent classifiers to produce an ensemble classifier that equals or matches existing classification abilities. I also explore the possibilities of unsupervised learning in making novel feature discovery in stars.
7
Hess, Andreas. "Supervised and unsupervised ensemble learning for the semantic web." [Mainz] [A. Hess], 2006. http://d-nb.info/99714971X/34.
Full text
8
Liu, Dongnan. "Supervised and Unsupervised Deep Learning-based Biomedical Image Segmentation." Thesis, The University of Sydney, 2021. https://hdl.handle.net/2123/24744.
Full textAbstract:
Biomedical image analysis plays a crucial role in the development of healthcare, with a wide scope of applications including the disease diagnosis, clinical treatment, and future prognosis. Among various biomedical image analysis techniques, segmentation is an essential step, which aims at assigning each pixel with labels of interest on the category and instance. At the early stage, the segmentation results were obtained via manual annotation, which is time-consuming and error-prone. Over the past few decades, hand-craft feature based methods have been proposed to segment the biomedical images automatically. However, these methods heavily rely on prior knowledge, which limits their generalization ability on various biomedical images. With the recent advance of the deep learning technique, convolutional neural network (CNN) based methods have achieved state-of-the-art performance on various nature and biomedical image segmentation tasks. The great success of the CNN based segmentation methods results from the ability to learn contextual and local information from the high dimensional feature space. However, the biomedical image segmentation tasks are particularly challenging, due to the complicated background components, the high variability of object appearances, numerous overlapping objects, and ambiguous object boundaries. To this end, it is necessary to establish automated deep learning-based segmentation paradigms, which are capable of processing the complicated semantic and morphological relationships in various biomedical images.
In this thesis, we propose novel deep learning-based methods for fully supervised and unsupervised biomedical image segmentation tasks. For the first part of the thesis, we introduce fully supervised deep learning-based segmentation methods on various biomedical image analysis scenarios. First, we design a panoptic structure paradigm for nuclei instance segmentation in the histopathology images, and cell instance segmentation in the fluorescence microscopy images. Traditional proposal-based and proposal-free instance segmentation methods are only capable to leverage either global contextual or local instance information. However, our panoptic paradigm integrates both of them and therefore achieves better performance. Second, we propose a multi-level feature fusion architecture for semantic neuron membrane segmentation in the electron microscopy (EM) images. Third, we propose a 3D anisotropic paradigm for brain tumor segmentation in magnetic resonance images, which enlarges the model receptive field while maintaining the memory efficiency.
Although our fully supervised methods achieve competitive performance on several biomedical image segmentation tasks, they heavily rely on the annotations of the training images. However, labeling pixel-level segmentation ground truth for biomedical images is expensive and labor-intensive. Subsequently, exploring unsupervised segmentation methods without accessing annotations is an important topic for biomedical image analysis. In the second part of the thesis, we focus on the unsupervised biomedical image segmentation methods. First, we proposed a panoptic feature alignment paradigm for unsupervised nuclei instance segmentation in the histopathology images, and mitochondria instance segmentation in EM images. To the best of our knowledge, we are for the first time to design an unsupervised deep learning-based method for various biomedical image instance segmentation tasks. Second, we design a feature disentanglement architecture for unsupervised object recognition. In addition to the unsupervised instance segmentation for the biomedical images, our method also achieves state-of-the-art performance on the unsupervised object detection for natural images, which further demonstrates its effectiveness and high generalization ability.
9
Nallabolu, Adithya Reddy. "Unsupervised Learning of Spatiotemporal Features by Video Completion." Thesis, Virginia Tech, 2017. http://hdl.handle.net/10919/79702.
Full textAbstract:
In this work, we present an unsupervised representation learning approach for learning rich spatiotemporal features from videos without the supervision from semantic labels. We propose to learn the spatiotemporal features by training a 3D convolutional neural network (CNN) using video completion as a surrogate task. Using a large collection of unlabeled videos, we train the CNN to predict the missing pixels of a spatiotemporal hole given the remaining parts of the video through minimizing per-pixel reconstruction loss. To achieve good reconstruction results using color videos, the CNN needs to have a certain level of understanding of the scene dynamics and predict plausible, temporally coherent contents. We further explore to jointly reconstruct both color frames and flow fields. By exploiting the statistical temporal structure of images, we show that the learned representations capture meaningful spatiotemporal structures from raw videos. We validate the effectiveness of our approach for CNN pre-training on action recognition and action similarity labeling problems. Our quantitative results demonstrate that our method compares favorably against learning without external data and existing unsupervised learning approaches.<br>Master of Science
10
Nasrin, Mst Shamima. "Pathological Image Analysis with Supervised and Unsupervised Deep Learning Approaches." University of Dayton / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1620052562772676.
Full text
11
Campbell, Benjamin W. "Supervised and Unsupervised Machine Learning Strategies for Modeling Military Alliances." The Ohio State University, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1558024695617708.
Full text
12
Liu, Yu. "Supervised and Unsupervised Learning for Semantics Distillation in Multimedia Processing." Thesis, State University of New York at Buffalo, 2018. http://pqdtopen.proquest.com/#viewpdf?dispub=10932367.
Full textAbstract:
<p> In linguistic, "semantics" stands for the intended meaning in natural language, such as in words, phrases and sentences. In this dissertation, the concept "semantics" is defined more generally: the intended meaning of information in all multimedia forms. The multimedia forms include language domain text, as well as vision domain stationary images and dynamic videos. Specifically, semantics in multimedia are the media content of cognitive information, knowledge and idea that can be represented in text, images and video clips. A narrative story, for example, can be semantics summary of a novel book, or semantics summary of the movie originated from that book. Thus, semantic is a high level abstract knowledge that is independent from multimedia forms. </p><p> Indeed, the same amount of semantics can be represented either redundantly or concisely, due to diversified levels of expression ability of multimedia. The process of a redundantly represented semantics evolving into a concisely represented one is called "semantic distillation". And this evolving process can happen either in between different multimedia forms, or within the same form. </p><p> The booming growth of unorganized and unfiltered information is bringing to people an unwanted issue, information overload, where techniques of semantic distillation are in high demand. However, as opportunities always be side with challenges, machine learning and Artificial Intelligence (AI) today become far more advanced than that in the past, and provide with us powerful tools and techniques. Large varieties of learning methods has made countless of impossible tasks come to reality. Thus in this dissertation, we take advantages of machine learning techniques, with both supervised learning and unsupervised learning, to empower the solving of semantics distillation problems. </p><p> Despite the promising future and powerful machine learning techniques, the heterogeneous forms of multimedia involving many domains still impose challenges to semantics distillation approaches. A major challenge is the definition of "semantics" and the related processing techniques can be entirely different from one problem to another. Varying types of multimedia resources can introduce varying kinds of domain-specific limitations and constraints, where the obtaining of semantics also becomes domain-specific. Therefore, in this dissertation, with text language and vision as the two major domains, we approach four problems of all combinations of the two domains: <b>• Language to Vision Domain:</b> In this study, <i>Presentation Storytelling </i> is formulated as a problem that suggesting the most appropriate images from online sources for storytelling purpose given a text query. Particularly, we approach the problem with a two-step semantics processing method, where the semantics from a simple query is first expanded to a diverse semantic graph, and then distilled from a large number of searched web photos to a few representative ones. This two-step method is empowered by Conditional Random Field (CRF) model, and learned in supervised manner with human-labeled examples. <b>• Vision to Language Domain:</b> The second study, <i> Visual Storytelling</i>, formulates a problem of generating a coherent paragraph from a photo stream. Different from presentation storytelling, visual storytelling goes in opposite way: the semantics extracted from a handful photos are distilled into text. In this dissertation, we address this problem by revealing the semantics relationships in visual domain, and distilled into language domain with a new designed Bidirectional Attention Recurrent Neural Network (BARNN) model. Particularly, an attention model is embedded to the RNN so that the coherence can be preserved in language domain at the output being a human-like story. The model is trained with deep learning and supervised learning with public datasets. <b>• Dedicated Vision Domain:</b> To directly approach the information overload issue in vision domain, <i> Image Semantic Extraction</i> formulates a problem that selects a subset from multimedia user's photo albums. In the literature, this problem has mostly been approached with unsupervised learning process. However, in this dissertation, we develop a novel supervised learning method to attach the same problem. We specify visual semantics as a quantizable variables and can be measured, and build an encoding-decoding pipeline with Long-Short-Term-Memory (LSTM) to model this quantization process. The intuition of encoding-decoding pipeline is to imitate human: read-think-and-retell. That is, the pipeline first includes an LSTM encoder scanning all photos for "reading" comprised semantics, then concatenates with an LSTM decoder selecting the most representative ones for "thinking" the gist semantics, finally adds a dedicated residual layer revisiting the unselected ones for "verifying" if the semantics are complete enough. <b> • Dedicated Language Domain:</b> Distinct from above issues, in this part, we introduce a different genre of machine learning method, unsupervised learning. We will address a semantics distillation problem in language domain, <i> Text Semantic Extraction</i>, where the semantics in a letter sequence are extracted from printed images. (Abstract shortened by ProQuest.) </p><p>
13
Doan, Charles A. "Connecting Unsupervised and Supervised Categorization Behavior from a Parainformative Perspective." Ohio University / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1521548439515138.
Full text
14
Kégl, Balazs. "Contributions to machine learning: the unsupervised, the supervised, and the Bayesian." Habilitation à diriger des recherches, Université Paris Sud - Paris XI, 2011. http://tel.archives-ouvertes.fr/tel-00674004.
Full text
15
Merat, Sepehr. "Clustering Via Supervised Support Vector Machines." ScholarWorks@UNO, 2008. http://scholarworks.uno.edu/td/857.
Full textAbstract:
An SVM-based clustering algorithm is introduced that clusters data with no a priori knowledge of input classes. The algorithm initializes by first running a binary SVM classifier against a data set with each vector in the set randomly labeled. Once this initialization step is complete, the SVM confidence parameters for classification on each of the training instances can be accessed. The lowest confidence data (e.g., the worst of the mislabeled data) then has its labels switched to the other class label. The SVM is then re-run on the data set (with partly re-labeled data). The repetition of the above process improves the separability until there is no misclassification. Variations on this type of clustering approach are shown.
16
Hussein, Abdul Aziz. "Identifying Crime Hotspot: Evaluating the suitability of Supervised and Unsupervised Machine learning." University of Cincinnati / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1624914607243042.
Full text
17
Zhang, Pin. "Nonlinear Semi-supervised and Unsupervised Metric Learning with Applications in Neuroimaging." Ohio University / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1525266545968548.
Full text
18
Amershi, Saleema Amin. "Combining unsupervised and supervised machine learning to build user models for intelligent learning environments." Thesis, University of British Columbia, 2007. http://hdl.handle.net/2429/31622.
Full textAbstract:
Traditional approaches to developing user models, especially for computer-based
learning environments, are notoriously difficult and time-consuming because they
rely heavily on expert-elicited knowledge about the target application and domain.
Furthermore, because the expert-elicited knowledge used in the user model is
application and domain specific, the entire model development process must be
repeated for each new application.
In this thesis, we outline a data-based user modeling framework that uses both
unsupervised and supervised machine learning in order to reduce the development
costs of building user models, and facilitate transferability. We apply the framework
to build user models of student interaction with two different learning environments
(the CIspace Constraint Satisfaction Problem Applet for demonstrating an Artificial
Intelligence algorithm, and the Adaptive Coach for Exploration for mathematical
functions), and using two different data sources (logged interface and eye-tracking
data). Although these two experiments are limited by the fact that we do not have
large data sets, our results provide initial evidence that (i) the framework can
automatically identify meaningful student interaction behaviors, and (ii) the user
models built via the framework can recognize new student behaviors online. In
addition, the similar results obtained from both of our experiments show framework
transferability across applications and data types.<br>Science, Faculty of<br>Computer Science, Department of<br>Graduate
19
Varshney, Varun. "Supervised and unsupervised learning for plant and crop row detection in precision agriculture." Thesis, Kansas State University, 2017. http://hdl.handle.net/2097/35463.
Full textAbstract:
Master of Science<br>Department of Computing and Information Sciences<br>William H. Hsu<br>The goal of this research is to present a comparison between different clustering and segmentation techniques, both supervised and unsupervised, to detect plant and crop rows. Aerial images, taken by an Unmanned Aerial Vehicle (UAV), of a corn field at various stages of growth were acquired in RGB format through the Agronomy Department at the Kansas State University. Several segmentation and clustering approaches were applied to these images, namely K-Means clustering, Excessive Green (ExG) Index algorithm, Support Vector Machines (SVM), Gaussian Mixture Models (GMM), and a deep learning approach based on Fully Convolutional Networks (FCN), to detect the plants present in the images. A Hough Transform (HT) approach was used to detect the orientation of the crop rows and rotate the images so that the rows became parallel to the x-axis. The result of applying different segmentation methods to the images was then used in estimating the location of crop rows in the images by using a template creation method based on Green Pixel Accumulation (GPA) that calculates the intensity profile of green pixels present in the images. Connected component analysis was then applied to find the centroids of the detected plants. Each centroid was associated with a crop row, and centroids lying outside the row templates were discarded as being weeds. A comparison between the various segmentation algorithms based on the Dice similarity index and average run-times is presented at the end of the work.
20
Vasiljeva, Polina. "Combining Unsupervised and Supervised Statistical Learning Methods for Currency Exchange Rate Forecasting." Thesis, KTH, Matematisk statistik, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-190984.
Full textAbstract:
In this thesis we revisit the challenging problem of forecasting currency exchange rate. We combine machine learning methods such as agglomerative hierarchical clustering and random forest to construct a two-step approach for predicting movements in currency exchange prices of the Swedish krona and the US dollar. We use a data set with over 200 predictors comprised of different financial and macro-economic time series and their transformations. We perform forecasting for one week ahead with different parameterizations and find a hit rate of on average 53%, with some of the parameterizations yielding hit rates as high as 60%. However, there is no clear indicator that there exists a combination of the methods and parameters that outperforms all of the tested cases. In addition, our results indicate that the two-step approach is sensitive to changes in the training set. This work has been conducted at the Third Swedish National Pension Fund (AP3) and KTH Royal Institute of Technology.<br>I denna uppsats analyserar vi det svårlösta problemet med att prognostisera utvecklingen för en valutakurs. Vi kombinerar maskininlärningsmetoder såsom agglomerativ hierarkisk klustring och Random Forest för att konstruera en modell i två steg med syfte att förutsäga utvecklingen av valutakursen mellan den svenska kronan och den amerikanska dollarn. Vi använder över 200 prediktorer bestående av olika finansiella och makroekonomiska tidsserier samt deras transformationer och utför prognoser för en vecka framåt med olika modellparametriseringar. En träffsäkerhet på i genomsnitt 53% erhålls, med några fall där en träffsäkerhet så hög som 60% kunde observeras. Det finns emellertid ingen tydlig indikation på att det existerar en kombination av de analyserade metoderna eller parametriseringarna som är optimal inom samtliga av de testade fallen. Vidare konstaterar vi att metoden är känslig för förändringar i underliggande träningsdata. Detta arbete har utförts på Tredje AP-fonden (AP3) och Kungliga Tekniska Högskolan (KTH).
21
Chu, Wen-Sheng. "Automatic Analysis of Facial Actions: Learning from Transductive, Supervised and Unsupervised Frameworks." Research Showcase @ CMU, 2017. http://repository.cmu.edu/dissertations/929.
Full textAbstract:
Automatic analysis of facial actions (AFA) can reveal a person’s emotion, intention, and physical state, and make possible a wide range of applications. To enable reliable, valid, and efficient AFA, this thesis investigates automatic analysis of facial actions through transductive, supervised and unsupervised learning. Supervised learning for AFA is challenging, in part, because of individual differences among persons in face shape and appearance and variation in video acquisition and context. To improve generalizability across persons, we propose a transductive framework, Selective Transfer Machine (STM), which personalizes generic classifiers through joint sample reweighting and classifier learning. By personalizing classifiers, STM offers improved generalization to unknown persons. As an extension, we develop a variant of STM for use when partially labeled data are available. Additional challenges for supervised learning include learning an optimal representation for classification, variation in base rates of action units (AUs), correlation between AUs and temporal consistency. While these challenges could be partly accommodated with an SVM or STM, a more powerful alternative is afforded by an end-to-end supervised framework (i.e., deep learning). We propose a convolutional network with long short-term memory (LSTM) and multi-label sampling strategies. We compared SVM, STM and deep learning approaches with respect to AU occurrence and intensity in and between BP4D+ [282] and GFT [93] databases, which consist of around 0.6 million annotated frames. Annotated video is not always possible or desirable. We introduce an unsupervised Branch-and-Bound framework to discover correlated facial actions in un-annotated video. We term this approach Common Event Discovery (CED). We evaluate CED in video and motion capture data. CED achieved moderate convergence with supervised approaches and enabled discovery of novel patterns occult to supervised approaches.
22
WU, CHEN. "OPTIMAL FEATURE SUBSET SELECTION ALGORITHMS FOR UNSUPERVISED LEARNING." University of Cincinnati / OhioLINK, 2000. http://rave.ohiolink.edu/etdc/view?acc_num=ucin974896296.
Full text
23
Abou-Moustafa, Karim. "Metric learning revisited: new approaches for supervised and unsupervised metric learning with analysis and algorithms." Thesis, McGill University, 2012. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=106370.
Full textAbstract:
In machine learning one is usually given a data set of real high dimensional vectors X, based on which it is desired to select a hypothesis θ from the space of hypotheses Θ using a learning algorithm. An immediate assumption that is usually imposed on X is that it is a subset from the very general embedding space Rp which makes the Euclidean distance ∥•∥2 to become the default metric for the elements of X. Since various learning algorithms assume that the input space is Rp with its endowed metric ∥•∥2 as a (dis)similarity measure, it follows that selecting hypothesis θ becomes intrinsically tied to the Euclidean distance. Metric learning is the problem of selecting a specific metric dX from a certain family of metrics D based on the properties of the elements in the set X. Under some performance measure, the metric dX is expected to perform better on X than any other metric d 2 D. If the learning algorithm replaces the very general metric ∥•∥2 with the metric dX , then selecting hypothesis θ will be tied to the more specific metric dX which carries all the information on the properties of the elements in X. In this thesis I propose two algorithms for learning the metric dX ; the first for supervised learning settings, and the second for unsupervised, as well as for supervised and semi-supervised settings. In particular, I propose algorithms that take into consideration the structure and geometry of X on one hand, and the characteristics of real world data sets on the other. However, if we are also seeking dimensionality reduction, then under some mild assumptions on the topology of X, and based on the available a priori information, one can learn an embedding for X into a low dimensional Euclidean space Rp0, p0 << p, where the Euclidean distance better reveals the similarities between the elements of X and their groupings (clusters). That is, as a by-product, we obtain dimensionality reduction together with metric learning. In the supervised setting, I propose PARDA, or Pareto discriminant analysis for discriminative linear dimensionality reduction. PARDA is based on the machinery of multi-objective optimization; simultaneously optimizing multiple, possibly conflicting, objective functions. This allows PARDA to adapt to the class topology in the lower dimensional space, and naturally handles the class masking problem that is inherent in Fisher's discriminant analysis framework for multiclass problems. As a result, PARDA yields significantly better classification results when compared with modern techniques for discriminative dimensionality reduction. In the unsupervised setting, I propose an algorithmic framework, denoted by ?? (note the different notation), that encapsulates spectral manifold learning algorithms and gears them for metric learning. The framework ?? captures the local structure and the local density information from each point in a data set, and hence it carries all the information on the varying sample density in the input space. The structure of ?? induces two distance metrics for its elements, the Bhattacharyya-Riemann metric dBR and the Jeffreys-Riemann metric dJR. Both metrics reorganize the proximity between the points in X based on the local structure and density around each point. As a result, when combining the metric space (??, dBR) or (??, dJR) with spectral clustering and Euclidean embedding, they yield significant improvements in clustering accuracies and error rates for a large variety of clustering and classification tasks.<br>Dans cette thèse, je propose deux algorithmes pour l'apprentissage de la métrique dX; le premier pour l'apprentissage supervisé, et le deuxième pour l'apprentissage non-supervisé, ainsi que pour l'apprentissage supervisé et semi-supervisé. En particulier, je propose des algorithmes qui prennent en considération la structure et la géométrie de X d'une part, et les caractéristiques des ensembles de données du monde réel d'autre part. Cependant, si on cherche également la réduction de dimension, donc sous certaines hypothèses légères sur la topologie de X, et en même temps basé sur des informations disponibles a priori, on peut apprendre une intégration de X dans un espace Euclidien de petite dimension Rp0 p0 << p, où la distance Euclidienne révèle mieux les ressemblances entre les éléments de X et leurs groupements (clusters). Alors, comme un sous-produit, on obtient simultanément une réduction de dimension et un apprentissage métrique. Pour l'apprentissage supervisé, je propose PARDA, ou Pareto discriminant analysis, pour la discriminante réduction linéaire de dimension. PARDA est basé sur le mécanisme d'optimisation à multi-objectifs; optimisant simultanément plusieurs fonctions objectives, éventuellement des fonctions contradictoires. Cela permet à PARDA de s'adapter à la topologie de classe dans un espace dimensionnel plus petit, et naturellement gère le problème de masquage de classe associé au discriminant Fisher dans le cadre d'analyse de problèmes à multi-classes. En conséquence, PARDA permet des meilleurs résultats de classification par rapport aux techniques modernes de réduction discriminante de dimension. Pour l'apprentissage non-supervisés, je propose un cadre algorithmique, noté par ??, qui encapsule les algorithmes spectraux d'apprentissage formant an algorithme d'apprentissage de métrique. Le cadre ?? capture la structure locale et la densité locale d'information de chaque point dans un ensemble de données, et donc il porte toutes les informations sur la densité d'échantillon différente dans l'espace d'entrée. La structure de ?? induit deux métriques de distance pour ses éléments: la métrique Bhattacharyya-Riemann dBR et la métrique Jeffreys-Riemann dJR. Les deux mesures réorganisent la proximité entre les points de X basé sur la structure locale et la densité autour de chaque point. En conséquence, lorsqu'on combine l'espace métrique (??, dBR) ou (??, dJR) avec les algorithmes de "spectral clustering" et "Euclidean embedding", ils donnent des améliorations significatives dans les précisions de regroupement et les taux d'erreur pour une grande variété de tâches de clustering et de classification.
24
Sahasrabudhe, Mihir. "Unsupervised and weakly supervised deep learning methods for computer vision and medical imaging." Thesis, université Paris-Saclay, 2020. http://www.theses.fr/2020UPASC010.
Full textAbstract:
Les premières contributions de cette thèse (Chapter 2 et Chapitre 3) sont des modèles appelés Deforming Autoencoder (DAE) et Lifting Autoencoder (LAE), utilisés pour l'apprentissage non-supervisé de l'alignement 2-D dense d'images d'une classe donnée, et à partir de cela, pour apprendre un modèle tridimensionnel de l'objet. Ces modèles sont capable d'identifer des espaces canoniques pour représenter de différent caractéristiques de l'objet, à savoir, l'apparence des objets dans l'espace canonique, la déformation dense associée permettant de retrouver l'image réelle à partir de cette apparence, et pour le cas des visages humains, le modèle 3-D propre au visage de la personne considérée, son expression faciale, et l'angle de vue de la caméra. De plus, nous illustrons l'application de DAE à d'autres domaines, à savoir, l'alignement d'IRM de poumons et d'images satellites. Dans le Chapitre 4, nous nous concentrons sur une problématique lié au cancer du sang-diagnostique d'hyperlymphocytosis. Nous proposons un modèle convolutif pour encoder les images appartenant à un patient, suivi par la concaténation de l'information contenue dans toutes les images. Nos résultats montrent que les modèles proposés sont de performances comparables à celles des biologistes, et peuvent dont les aider dans l'élaboration de leur diagnostique<br>The first two contributions of this thesis (Chapter 2 and 3) are models for unsupervised 2D alignment and learning 3D object surfaces, called Deforming Autoencoders (DAE) and Lifting Autoencoders (LAE). These models are capable of identifying canonical space in order to represent different object properties, for example, appearance in a canonical space, deformation associated with this appearance that maps it to the image space, and for human faces, a 3D model for a face, its facial expression, and the angle of the camera. We further illustrate applications of models to other domains_ alignment of lung MRI images in medical image analysis, and alignment of satellite images for remote sensing imagery. In Chapter 4, we concentrate on a problem in medical image analysis_ diagnosis of lymphocytosis. We propose a convolutional network to encode images of blood smears obtained from a patient, followed by an aggregation operation to gather information from all images in order to represent them in one feature vector which is used to determine the diagnosis. Our results show that the performance of the proposed models is at-par with biologists and can therefore augment their diagnosis
25
Riverain, Paul. "Integrating prior knowledge into unsupervised learning for railway transportation." Electronic Thesis or Diss., Université Paris Cité, 2022. http://www.theses.fr/2022UNIP7326.
Full textAbstract:
Dans un réseau de transport, la supervision joue un rôle essentiel pour assurer le bon déroulement des opérations et la satisfaction des voyageurs. Cela inclut la fourniture d'informations adéquates aux passagers, la gestion de la sécurité des passagers, des actifs fixes, des systèmes de traction et la supervision du trafic en temps réel. Dans cette thèse, nous abordons la conception de nouveaux outils algorithmiques orientés données pour aider les opérateurs des systèmes ferroviaires urbains dans leur tâche de supervision du réseau de transport. Dans la mesure où beaucoup de décisions des opérateurs dépendent de la façon dont les déplacements des usagers sont distribués sur le réseau, nous cherchons donc à fournir aux opérateurs des informations synthétiques sur le flux de passagers actuel et son évolution.Etant données les entrées et sorties des usagers sur le réseau, le flux de passagers peut être vu comme un graphe dynamique en temps discret dont les nœuds sont les stations du réseau et dont les arêtes comptent le nombre de passagers entre deux paires de stations. Nous cherchons donc à résumer ce graphe dynamique en utilisant des techniques de clustering. Les modèles à blocs, dont font partie le modèle à blocs stochastiques et le modèle à blocs latents, sont des approches probabilistes pour le co-clustering qui semblent adéquates pour cette tâche de clustering de graphe. Le clustering ne dépend ici que de la façon dont le flux de passagers est distribué sur le réseau et n'inclut pas les connaissances expertes des opérateurs. Par conséquent, nous cherchons également à prendre en compte des informations contextuelles telles que les caractéristiques des stations, la topologie du réseau ou les actions des opérateurs sur la régulation des trains dans la synthèse du flux de passagers. Nous passons d'abord en revue les principaux concepts sur lesquels nos travaux sont basés ainsi que certains travaux connexes sur l'apprentissage non supervisé pour l'analyse des flux de passagers. Nous proposons ensuite une formalisation du problème opérationnel. Dans notre première contribution, nous présentons une extension du modèle à blocs stochastiques (SBM) pour les graphes dynamiques à temps discret qui prend en compte la variabilité des degrés des nœuds, ce qui nous permet de modéliser une classe plus large de réseaux. Nous dérivons une procédure d'inférence basée sur un algorithme EM variationnel qui fournit également les moyens d'estimer les corrections de degré en fonction du temps. Dans notre deuxième contribution, nous proposons d'exploiter les connaissances préalables sous la forme d'une semi-supervision par paire dans l'espace des lignes et des colonnes afin d'améliorer les performances de classification des algorithmes dérivés du modèle à blocs latents (LBM). Nous introduisons un cadre probabiliste général pour incorporer les relations Must Link et Cannot Link dans le LBM basé sur les champs aléatoires de Markov cachés et présentons deux algorithmes d'inférence basés sur EM variationnel et EM classifiant. Enfin, nous présentons l'application des deux algorithmes précédents sur des données de flux de passagers du monde réel. Nous décrivons ensuite un outil interactif que nous avons créé pour visualiser les clusters obtenus avec un LBM dynamique et les interpréter en utilisant les paramètres estimés du modèle. Ensuite, nous appliquons les algorithmes de co-clustering sur trois échelles de temps différentes. Nous présentons les aspects pratiques liés à l'utilisation de ces algorithmes ainsi que les cas d'utilisation possibles de la supervision par paire. Enfin, nous détaillons les limites des algorithmes proposés et présentons quelques perspectives<br>In a transportation network, supervision plays a key role to ensure smooth operations and satisfied voyagers. This includes providing adequate passenger information, managing the security of the passengers, the fixed assets, the traction power systems and supervising the traffic in real-time. In this thesis, we address the conception of new data-driven algorithmic tools to help urban railway operators in the task of supervision of the transportation network. As many decisions of the operators depend on how the trips of the users are distributed on the network, we seek to provide synthetic information about the current passenger flow and its evolution to the operators in order to help them in the supervision of the traffic and the fixed assets. Given the entries and exits of the users on the network, the passenger flow can be seen as a discrete-time dynamic graph whose nodes are the stations of the network and whose edges count the number of passengers between any two pairs of stations. We thus aim at summarizing this dynamic graph using clustering techniques. The Block Models, including the Stochastic Block Model and the Latent Block Model, are model-based approaches for co-clustering that appear adequate for this task of graph clustering. The clustering here only depends on how the passenger flow is distributed on the network and does not include the expert knowledge of the operators. Consequently, we also seek to take into account contextual information such as the stations characteristics, the network topology or the actions of the operators on the train regulation in the summarizing of the passenger flow. We first review the main concepts our works are based on as well as some related works on unsupervised learning for passenger flow analysis. We then propose a formalization of the operational problem. In our first contribution, we present an extension of the Stochastic Block Model (SBM) for discrete-time dynamic networks that takes into account the variability in node degrees, allowing us to model a broader class of networks. We derive an inference procedure based on Variational Expectation-Maximization that also provides the means to estimate the time-dependent degree corrections. For our second contribution, we propose to leverage prior knowledge in the form of pairwise semi-supervision in both row and column space to improve the clustering performances of the algorithms derived from the Latent Block Model (LBM). We introduce a general probabilistic framework for incorporating Must Link and Cannot Link relationships in the LBM based on Hidden Markov Random Fields and present two inference algorithms based on Variational and Classification EM. Finally, we present the application of the two previous algorithms on real-world passenger flow data. We then describe an interactive tool that we created to visualize the clusters obtained with the dynamic LBM and interpret them using the estimated parameters of the model. Next, we apply the co-clustering algorithms in three different contexts to analyze the passenger flow on different time scales. We present the practical aspects related to the utilization of these algorithms as well as possible use-cases the pairwise supervision. Finally, we detail the limits of the proposed algorithms and present some perspectives
26
Walsh, Andrew Michael Graduate school of biomedical engineering UNSW. "Application of supervised and unsupervised learning to analysis of the arterial pressure pulse." Awarded by:University of New South Wales. Graduate school of biomedical engineering, 2006. http://handle.unsw.edu.au/1959.4/24841.
Full textAbstract:
This thesis presents an investigation of statistical analytical methods applied to the analysis of the shape of the arterial pressure waveform. The arterial pulse is analysed by a selection of both supervised and unsupervised methods of learning. Supervised learning methods are generally better known as regression. Unsupervised learning methods seek patterns in data without the specification of a target variable. The theoretical relationship between arterial pressure and wave shape is first investigated by study of a transmission line model of the arterial tree. A meta-database of pulse waveforms obtained by the SphygmoCor"??" device is then analysed by the unsupervised learning technique of Self Organising Maps (SOM). The map patterns indicate that the observed arterial pressures affect the wave shape in a similar way as predicted by the theoretical model. A database of continuous arterial pressure obtained by catheter line during sleep is used to derive supervised models that enable estimation of arterial pressures, based on the measured wave shapes. Independent component analysis (ICA) is also used in a supervised learning methodology to show the theoretical plausibility of separating the pressure signals from unwanted noise components. The accuracy and repeatability of the SphygmoCor?? device is measured and discussed. Alternative regression models are introduced that improve on the existing models in the estimation of central cardiovascular parameters from peripheral arterial wave shapes. Results of this investigation show that from the information in the wave shape, it is possible, in theory, to estimate the continuous underlying pressures within the artery to a degree of accuracy acceptable to the Association for the Advancement of Medical Instrumentation. This could facilitate a new role for non-invasive sphygmographic devices, to be used not only for feature estimation but as alternatives to invasive arterial pressure sensors in the measurement of continuous blood pressure.
27
Feng, Zeyu. "Learning Deep Representations from Unlabelled Data for Visual Recognition." Thesis, The University of Sydney, 2021. https://hdl.handle.net/2123/26876.
Full textAbstract:
Self-supervised learning (SSL) aims at extracting from abundant unlabelled images transferable semantic features, which benefit various downstream visual tasks by reducing the sample complexity when human annotated labels are scarce. SSL is promising because it also boosts performance in diverse tasks when combined with the knowledge of existing techniques. Therefore, it is important and meaningful to study how SSL leads to better transferability and design novel SSL methods.
To this end, this thesis proposes several methods to improve SSL and its function in downstream tasks. We begin by investigating the effect of unlabelled training data, and introduce an information-theoretical constraint for SSL from multiple related domains. In contrast to conventional single dataset, exploiting multi-domains has the benefits of decreasing the build-in bias of individual domain and allowing knowledge transfer across domains. Thus, the learned representation is more unbiased and transferable.
Next, we describe a feature decoupling (FD) framework that incorporates invariance into predicting transformations, one main category of SSL methods, by observing that they often lead to co-variant features unfavourable for transfer. Our model learns a split representation that contains both transformation related and unrelated parts. FD achieves SOTA results on SSL benchmarks. We also present a multi-task method with theoretical understanding for contrastive learning, the other main category of SSL, by leveraging the semantic information from synthetic images to facilitate the learning of class-related semantics.
Finally, we explore self-supervision in open-set unsupervised classification with the knowledge of source domain. We propose to enforce consistency under transformation of target data and discover pseudo-labels from confident predictions. Experimental results outperform SOTA open-set domain adaptation methods.
28
Charles, Eugene Yougarajah Andrew. "Supervised and unsupervised weight and delay adaptation learning in temporal coding spiking neural networks." Thesis, Cardiff University, 2006. http://orca.cf.ac.uk/56168/.
Full textAbstract:
Artificial neural networks are learning paradigms which mimic the biological neural system. The temporal coding Spiking Neural Network, a relatively new artificial neural network paradigm, is considered to be computationally more powerful than the conventional neural network. Research on the network of spiking neurons is an emerging field and has potential for wider investigation. This research explores alternative learning models with temporal coding spiking neural networks for clustering and classification tasks. Neurons are known to be operating in two modes namely, as integrators and coincidence detectors. Previous temporal coding spiking neural networks, realising spiking neurons as integrators, were utilised for analytical studies. Temporal coding spiking neural networks applied successfully for clustering and classification tasks realised spiking neurons as coincidence detectors and encoded input in formation in the connection delays through a weight adaptation technique. These learning models select suitably delayed connections by enhancing the weights of those connections while weakening the others. This research investigates the learning in temporal coding spiking neural networks with spiking neurons as integrators and coincidence detectors. Focus is given to both supervised and unsupervised learning through weight as well as through delay adaptation. Three novel models for learning in temporal coding spiking neural networks are presented in this research. The first spiking neural network model, Self- Organising Weight Adaptation Spiking Neural Network (SOWA_SNN) realises the spiking neuron as integrator. This model adapts and encodes input information in its connection weights. The second learning model, Self-Organising Delay Adaptation Spiking Neural Network (SODA_SNN) and the third model, Super vised Delay Adaptation Spiking Neural Network (SDA_SNN) realise the spiking neuron as coincidence detector. These two models adapt the connection delays in order to detect temporal patterns through coincidence detection. The first two models were developed for clustering applications and the third for classification tasks. All three models employ Hebbian-based learning rules to update the network connection parameters by utilising the difference between the input and output spike times. The proposed temporal coding spiking neural network models were implemented as discrete models in software and their characteristics and capabilities were analysed through simulations on three bench mark data sets and a high dimensional data set. All three models were able to cluster or classify the analysed data sets efficiently with a high degree of accuracy. The performance of the proposed models, was found to be better than the existing spiking neural network models as well as conventional neural networks. The proposed learning paradigms could be applied to a wide range of applications including manufacturing, business and biomedical domains.
29
Rostamniakankalhori, Sharareh. "Integrated supervised and unsupervised learning method to predict the outcome of tuberculosis treatment course." Thesis, University of Manchester, 2011. https://www.research.manchester.ac.uk/portal/en/theses/integrated-supervised-and-unsupervised-learning-method-to-predict-the-outcome-of-tuberculosis-treatment-course(8a2f7033-3c27-4c6e-a575-85b90a547086).html.
Full textAbstract:
Tuberculosis (TB) is an infectious disease which is a global public health problem with over 9 million new cases annually. Tuberculosis treatment, with patient supervision and support is an element of the global plan to stop TB designed by the World Health Organization in 2006. The plan requires prediction of patient treatment course destination. The prediction outcome can be used to determine how intensive the level of supplying services and supports in frame of DOTS therapy should be. No predictive model for the outcome has been developed yet and only limited reports of influential factors for considered outcome are available. To fill this gap, this thesis develops a machine learning approach to predict the outcome of tuberculosis treatment course, which includes, firstly, data of 6,450 Iranian TB patients under DOTS (directly observed treatment, short course ) therapy were analysed to initially diagnose the significant predictors by correlation analysis; secondly, these significant features were applied to find the best classification approach from six examined algorithms including decision tree, Bayesian network, logistic regression, multilayer perceptron, radial basis function, and support vector machine; thirdly, the prediction accuracy of these existing techniques was improved by proposing and developing a new integrated method of k-mean clustering and classification algorithms. Finally, a cluster-based simplified decision tree (CSDT) was developed through an innovative hierarchical clustering and classification algorithm. CSDT was built by k-mean partitioning and the decision tree learning. This innovative method not only improves the prediction accuracy significantly but also leads to a much simpler and interpretative decision tree. The main results of this study included, firstly, finding seventeen significantly correlated features which were: age, sex, weight, nationality, area of residency, current stay in prison, low body weight, TB type, treatment category, length of disease, TB case type, recent TB infection, diabetic or HIV positive, and social risk factors like history of imprisonment, IV drug usage, and unprotected sex ; secondly, the results by applying and comparing six applied supervised machine learning tools on the testing set revealed that decision trees gave the best prediction accuracy (74.21%) compared with other methods; thirdly, by using testing set, the new integrated approach to combine the clustering and classification approach leads to the prediction accuracy improvement for all applied classifiers; the most and least improvement for prediction accuracy were shown by logistic regression (10%) and support vector machine (4%) respectively. Finally, by applying the proposed and developed CSDT, cluster-based simplified decision trees were optioned, which reduced the size of the resulting decision tree and further improved the prediction accuracy. Data type and having normal distribution have created an opportunity for the decision tree to outperform other algorithms. Pre-learning by k-mean clustering to relocate the objects and put similar cases in the same group can improve the classification accuracy. The compatible feature of k-mean partitioning and decision tree to generate pure local regions can simplify the decision trees and make them more precise through creating smaller sub-trees with fewer misclassified cases. The extracted rules from these trees can play the role of a knowledge base for a decision support system in further studies.
30
Doersch, Carl. "Supervision Beyond Manual Annotations for Learning Visual Representations." Research Showcase @ CMU, 2016. http://repository.cmu.edu/dissertations/787.
Full textAbstract:
For both humans and machines, understanding the visual world requires relating new percepts with past experience. We argue that a good visual representation for an image should encode what makes it similar to other images, enabling the recall of associated experiences. Current machine implementations of visual representations can capture some aspects of similarity, but fall far short of human ability overall. Even if one explicitly labels objects in millions of images to tell the computer what should be considered similar—a very expensive procedure—the labels still do not capture everything that might be relevant. This thesis shows that one can often train a representation which captures similarity beyond what is labeled in a given dataset. That means we can begin with a dataset that has uninteresting labels, or no labels at all, and still build a useful representation. To do this, we propose to using pretext tasks: tasks that are not useful in and of themselves, but serve as an excuse to learn a more general-purpose representation. The labels for a pretext task can be inexpensive or even free. Furthermore, since this approach assumes training labels differ from the desired outputs, it can handle output spaces where the correct answer is ambiguous, and therefore impossible to annotate by hand. The thesis explores two broad classes of supervision. The first isweak image-level supervision, which is exploited to train mid-level discriminative patch classifiers. For example, given a dataset of street-level imagery labeled only with GPS coordinates, patch classifiers are trained to differentiate one specific geographical region (e.g. the city of Paris) from others. The resulting classifiers each automatically collect and associate a set of patches which all depict the same distinctive architectural element. In this way, we can learn to detect elements like balconies, signs, and lamps without annotations. The second type of supervision requires no information about images other than the pixels themselves. Instead, the algorithm is trained to predict the context around image patches. The context serves as a sort of weak label: to predict well, the algorithm must associate similar-looking patches which also have similar contexts. After training, the feature representation learned using this within-image context indeed captures visual similarity across images, which ultimately makes it useful for real tasks like object detection and geometry estimation.
31
Rothe, Sascha [Verfasser], and Hinrich [Akademischer Betreuer] Schütze. "Supervised and unsupervised methods for learning representations of linguistic units / Sascha Rothe ; Betreuer: Hinrich Schütze." München : Universitätsbibliothek der Ludwig-Maximilians-Universität, 2017. http://d-nb.info/1137226641/34.
Full text
32
Kalhori, Sharareh Rostam Niakan. "An integrated supervised and unsupervised learning approach to predict the outcome of tuberculosis treatment course." Thesis, University of Manchester, 2011. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.542693.
Full text
33
Sharma, Alok. "Linear Models for Dimensionality Reduction and Statistical Pattern Recognition for Supervised and Unsupervised Tasks." Thesis, Griffith University, 2006. http://hdl.handle.net/10072/365298.
Full textAbstract:
In this dissertation a number of novel algorithms for dimension reduction and statistical pattern recognition for both supervised and unsupervised learning tasks have been presented. Several existing pattern classifiers and dimension reduction algorithms are studied. Their limitations and/or weaknesses are considered and accordingly improved techniques are given which overcome several of their shortcomings. In particular, the following research works are carried out:
• Literature survey of basic techniques for pattern classification like Gaussian mixture model (GMM), expectation-maximization (EM) algorithm, minimum distance classifier (MDC), vector quantization (VQ), nearest neighbour (NN) and k-nearest neighbour (kNN) are conducted.
• Survey of basic dimensional reduction tools viz. principal component analysis (PCA) and linear discriminant analysis (LDA) are conducted. These techniques are also considered for pattern classification purposes.
• Development of Fast PCA technique which finds the desired number of leading eigenvectors with much less computational cost and requires extremely low processing time as compared to the basic PCA model.
• Development of gradient LDA technique which solves the small sample size problem as was not possible by basic LDA technique.
• The rotational LDA technique is developed which efficiently reduces the overlapping of samples between the classes to a large extent as compared to the basic LDA technique.
• A combined classifier using MDC, class-dependent PCA and LDA is designed which improves the performance of the classifier which was not possible by using single classifiers. The application of PCA prior to LDA is conducted in such a way that it avoids small sample size problem (if present).
• The splitting technique initialization is introduced in the local PCA technique. The proposed integration enables easier data processing and more accurate representation of multivariate data.
• A combined technique using VQ and vector quantized principal component analysis (VQPCA) is presented which provides significant improvement in the classifier performance (in terms of accuracy) at very low storage and processing time requirements compared to individual and several other classifiers.
• Survey on unsupervised learning task like independent component analysis (ICA) is conducted.
• A new perspective of subspace ICA (generalized ICA, where all the components need not be independent) is introduced by developing vector kurtosis (an extension of kurtosis) function.<br>Thesis (PhD Doctorate)<br>Doctor of Philosophy (PhD)<br>Griffith School of Engineering<br>Full Text
34
Olley, Peter. "Learning techniques for expert systems : an investigation, using simulation techniques, into the possibilities and requirements for reliable un-supervised learning for industrial expert systems." Thesis, University of Bradford, 1992. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.320422.
Full text
35
Alirezaie, Marjan. "Semantic Analysis Of Multi Meaning Words Using Machine Learning And Knowledge Representation." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-70086.
Full textAbstract:
The present thesis addresses machine learning in a domain of naturallanguage phrases that are names of universities. It describes two approaches to this problem and a software implementation that has made it possible to evaluate them and to compare them. In general terms, the system's task is to learn to 'understand' the significance of the various components of a university name, such as the city or region where the university is located, the scienti c disciplines that are studied there, or the name of a famous person which may be part of the university name. A concrete test for whether the system has acquired this understanding is when it is able to compose a plausible university name given some components that should occur in the name. In order to achieve this capability, our system learns the structure of available names of some universities in a given data set, i.e. it acquires a grammar for the microlanguage of university names. One of the challenges is that the system may encounter ambiguities due to multi meaning words. This problem is addressed using a small ontology that is created during the training phase. Both domain knowledge and grammatical knowledge is represented using decision trees, which is an ecient method for concept learning. Besides for inductive inference, their role is to partition the data set into a hierarchical structure which is used for resolving ambiguities. The present report also de nes some modi cations in the de nitions of parameters, for example a parameter for entropy, which enable the system to deal with cognitive uncertainties. Our method for automatic syntax acquisition, ADIOS, is an unsupervised learning method. This method is described and discussed here, including a report on the outcome of the tests using our data set. The software that has been implemented and used in this project has been implemented in C.
36
Osgood, Thomas J. "Semantic labelling of road scenes using supervised and unsupervised machine learning with lidar-stereo sensor fusion." Thesis, University of Warwick, 2013. http://wrap.warwick.ac.uk/60439/.
Full textAbstract:
At the highest level the aim of this thesis is to review and develop reliable and efficient algorithms for classifying road scenery primarily using vision based technology mounted on vehicles. The purpose of this technology is to enhance vehicle safety systems in order to prevent accidents which cause injuries to drivers and pedestrians. This thesis uses LIDAR–stereo sensor fusion to analyse the scene in the path of the vehicle and apply semantic labels to the different content types within the images. It details every step of the process from raw sensor data to automatically labelled images. At each stage of the process currently used methods are investigated and evaluated. In cases where existingmethods do not produce satisfactory results improvedmethods have been suggested. In particular, this thesis presents a novel, automated,method for aligning LIDAR data to the stereo camera frame without the need for specialised alignment grids. For image segmentation a hybrid approach is presented, combining the strengths of both edge detection and mean-shift segmentation. For texture analysis the presented method uses GLCM metrics which allows texture information to be captured and summarised using only four feature descriptors compared to the 100’s produced by SURF descriptors. In addition to texture descriptors, the ìD information provided by the stereo system is also exploited. The segmented point cloud is used to determine orientation and curvature using polynomial surface fitting, a technique not yet applied to this application. Regarding classification methods a comprehensive study was carried out comparing the performance of the SVM and neural network algorithms for this particular application. The outcome shows that for this particular set of learning features the SVM classifiers offer slightly better performance in the context of image and depth based classification which was not made clear in existing literature. Finally a novel method of making unsupervised classifications is presented. Segments are automatically grouped into sub-classes which can then be mapped to more expressive super-classes as needed. Although the method in its current state does not yet match the performance of supervised methods it does produce usable classification results without the need for any training data. In addition, the method can be used to automatically sub-class classes with significant inter-class variation into more specialised groups prior to being used as training targets in a supervised method.
37
Mysore, Gopinath Abhijith Athreya. "Automatic Detection of Section Title and Prose Text in HTML Documents Using Unsupervised and Supervised Learning." University of Cincinnati / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1535371714338677.
Full text
38
Kilinc, Ismail Ozsel. "Graph-based Latent Embedding, Annotation and Representation Learning in Neural Networks for Semi-supervised and Unsupervised Settings." Scholar Commons, 2017. https://scholarcommons.usf.edu/etd/7415.
Full textAbstract:
Machine learning has been immensely successful in supervised learning with outstanding examples in major industrial applications such as voice and image recognition. Following these developments, the most recent research has now begun to focus primarily on algorithms which can exploit very large sets of unlabeled examples to reduce the amount of manually labeled data required for existing models to perform well. In this dissertation, we propose graph-based latent embedding/annotation/representation learning techniques in neural networks tailored for semi-supervised and unsupervised learning problems. Specifically, we propose a novel regularization technique called Graph-based Activity Regularization (GAR) and a novel output layer modification called Auto-clustering Output Layer (ACOL) which can be used separately or collaboratively to develop scalable and efficient learning frameworks for semi-supervised and unsupervised settings.
First, singularly using the GAR technique, we develop a framework providing an effective and scalable graph-based solution for semi-supervised settings in which there exists a large number of observations but a small subset with ground-truth labels. The proposed approach is natural for the classification framework on neural networks as it requires no additional task calculating the reconstruction error (as in autoencoder based methods) or implementing zero-sum game mechanism (as in adversarial training based methods). We demonstrate that GAR effectively and accurately propagates the available labels to unlabeled examples. Our results show comparable performance with state-of-the-art generative approaches for this setting using an easier-to-train framework.
Second, we explore a different type of semi-supervised setting where a coarse level of labeling is available for all the observations but the model has to learn a fine, deeper level of latent annotations for each one. Problems in this setting are likely to be encountered in many domains such as text categorization, protein function prediction, image classification as well as in exploratory scientific studies such as medical and genomics research. We consider this setting as simultaneously performed supervised classification (per the available coarse labels) and unsupervised clustering (within each one of the coarse labels) and propose a novel framework combining GAR with ACOL, which enables the network to perform concurrent classification and clustering. We demonstrate how the coarse label supervision impacts performance and the classification task actually helps propagate useful clustering information between sub-classes. Comparative tests on the most popular image datasets rigorously demonstrate the effectiveness and competitiveness of the proposed approach.
The third and final setup builds on the prior framework to unlock fully unsupervised learning where we propose to substitute real, yet unavailable, parent- class information with pseudo class labels. In this novel unsupervised clustering approach the network can exploit hidden information indirectly introduced through a pseudo classification objective. We train an ACOL network through this pseudo supervision together with unsupervised objective based on GAR and ultimately obtain a k-means friendly latent representation. Furthermore, we demonstrate how the chosen transformation type impacts performance and helps propagate the latent information that is useful in revealing unknown clusters. Our results show state-of-the-art performance for unsupervised clustering tasks on MNIST, SVHN and USPS datasets with the highest accuracies reported to date in the literature.
39
Hübner, David [Verfasser], and Michael W. [Akademischer Betreuer] Tangermann. "From supervised to unsupervised machine learning methods for brain-computer interfaces and their application in language rehabilitation." Freiburg : Universität, 2020. http://d-nb.info/1206095768/34.
Full text
40
Govindarajan, Hariprasath. "Self-Supervised Representation Learning for Content Based Image Retrieval." Thesis, Linköpings universitet, Statistik och maskininlärning, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-166223.
Full textAbstract:
Automotive technologies and fully autonomous driving have seen a tremendous growth in recent times and have benefitted from extensive deep learning research. State-of-the-art deep learning methods are largely supervised and require labelled data for training. However, the annotation process for image data is time-consuming and costly in terms of human efforts. It is of interest to find informative samples for labelling by Content Based Image Retrieval (CBIR). Generally, a CBIR method takes a query image as input and returns a set of images that are semantically similar to the query image. The image retrieval is achieved by transforming images to feature representations in a latent space, where it is possible to reason about image similarity in terms of image content. In this thesis, a self-supervised method is developed to learn feature representations of road scenes images. The self-supervised method learns feature representations for images by adapting intermediate convolutional features from an existing deep Convolutional Neural Network (CNN). A contrastive approach based on Noise Contrastive Estimation (NCE) is used to train the feature learning model. For complex images like road scenes where mutiple image aspects can occur simultaneously, it is important to embed all the salient image aspects in the feature representation. To achieve this, the output feature representation is obtained as an ensemble of feature embeddings which are learned by focusing on different image aspects. An attention mechanism is incorporated to encourage each ensemble member to focus on different image aspects. For comparison, a self-supervised model without attention is considered and a simple dimensionality reduction approach using SVD is treated as the baseline. The methods are evaluated on nine different evaluation datasets using CBIR performance metrics. The datasets correspond to different image aspects and concern the images at different spatial levels - global, semi-global and local. The feature representations learned by self-supervised methods are shown to perform better than the SVD approach. Taking into account that no labelled data is required for training, learning representations for road scenes images using self-supervised methods appear to be a promising direction. Usage of multiple query images to emphasize a query intention is investigated and a clear improvement in CBIR performance is observed. It is inconclusive whether the addition of an attentive mechanism impacts CBIR performance. The attention method shows some positive signs based on qualitative analysis and also performs better than other methods for one of the evaluation datasets containing a local aspect. This method for learning feature representations is promising but requires further research involving more diverse and complex image aspects.
41
Morbieu, Stanislas. "Leveraging textual embeddings for unsupervised learning." Electronic Thesis or Diss., Université Paris Cité, 2020. http://www.theses.fr/2020UNIP5191.
Full textAbstract:
Les données textuelles constituent un vivier d'information exploitable pour de nombreuses entreprises. En particulier, le web fournit une source quasiment inépuisable de données textuelles qui peuvent être utilisées à profit pour des systèmes de recommandation, de veille, de recherche d'information, etc. Les récentes avancées en traitement du langage naturel ont permit de capturer le sens des mots dans leur contexte afin d'améliorer les systèmes de traduction, de résumés, ou encore le regroupement de documents suivant des catégories prédéfinies. La majorité de ces applications reposent cependant souvent sur une intervention humaine non négligeable pour annoter des corpus : Elle consiste, par exemple, à fournir aux algorithmes des exemples d'affectation de catégories à des documents. L'algorithme apprend donc à reproduire le jugement humain et l'applique pour de nouveaux documents. L'objet de cette thèse est de tirer profit des dernières avancées qui capturent l'information sémantique du texte pour l'appliquer dans un cadre non supervisé. Les contributions s'articulent autour de trois axes principaux. Dans le premier, nous proposons une méthode pour transférer l'information capturée par un réseau neuronal pour de la classification croisée textuelle. Elle consiste à former simultanément des groupes de documents similaires et des groupes de mots cohérents. Ceci facilite l'interprétation d'un grand corpus puisqu'on peut caractériser des groupes de documents par des groupes de mots, résumant ainsi une grande volumétrie de texte. Plus précisément nous entraînons l'algorithme Paragraph Vectors sur un jeu de données augmenté en faisant varier les différents hyperparamètres, classifions les documents à partir des différentes représentations vectorielles obtenues et cherchons un consensus sur des différentes partitions. Une classification croisée contrainte de la matrice de co-occurrences termes-documents est ensuite appliquée pour conserver le partitionnement consensus obtenu. Cette méthode se révèle significativement meilleure en qualité de partitionnement des documents sur des corpus variés et a l'avantage de l'interprétation offerte par la classification croisée. Deuxièmement, nous présentons une méthode pour évaluer des algorithmes de classification croisée en exploitant des représentation vectorielles de mots appelées word embeddings. Il s’agit de vecteurs construits grâce à de gros volumes de textes, dont une caractéristique majeure est que deux mots sémantiquement proches ont des word embeddings proches selon une distance cosinus. Notre méthode permet de mesurer l'adéquation entre les partitions de documents et de mots, offrant ainsi de manière totalement non supervisée un indice de la qualité de la classification croisée. Troisièmement, nous proposons un système qui permet de recommander des petites annonces similaires lorsqu'on en consulte une. Leurs descriptions sont souvent courtes, syntaxiquement incorrectes, et l'utilisation de synonymes font qu'il est difficile pour des systèmes traditionnels de mesurer fidèlement la similarité sémantique. De plus, le fort renouvellement des annonces encore valides (produit non vendu) implique des choix permettant d’avoir un faible temps de calcul. Notre méthode, simple à implémenter, répond à ce cas d'usage et s'appuie de nouveau sur les word embeddings. L'utilisation de ceux-ci présente certains avantages mais impliquent également quelques difficultés : la création de tels vecteurs nécessite de choisir les valeurs de certains paramètres, et la différence entre le corpus sur lequel les word embeddings ont été construit et celui sur lequel ils sont utilisés fait émerger le problème des mots qui n'ont pas de représentation vectorielle. Nous présentons, pour palier ces problèmes, une analyse de l'impact des différents paramètres sur les word embeddings ainsi qu'une étude des méthodes permettant de traiter le problème de « mots en dehors du vocabulaire »<br>Textual data is ubiquitous and is a useful information pool for many companies. In particular, the web provides an almost inexhaustible source of textual data that can be used for recommendation systems, business or technological watch, information retrieval, etc. Recent advances in natural language processing have made possible to capture the meaning of words in their context in order to improve automatic translation systems, text summary, or even the classification of documents according to predefined categories. However, the majority of these applications often rely on a significant human intervention to annotate corpora: This annotation consists, for example in the context of supervised classification, in providing algorithms with examples of assigning categories to documents. The algorithm therefore learns to reproduce human judgment in order to apply it for new documents. The object of this thesis is to take advantage of these latest advances which capture the semantic of the text and use it in an unsupervised framework. The contributions of this thesis revolve around three main axes. First, we propose a method to transfer the information captured by a neural network for co-clustering of documents and words. Co-clustering consists in partitioning the two dimensions of a data matrix simultaneously, thus forming both groups of similar documents and groups of coherent words. This facilitates the interpretation of a large corpus of documents since it is possible to characterize groups of documents by groups of words, thus summarizing a large corpus of text. More precisely, we train the Paragraph Vectors algorithm on an augmented dataset by varying the different hyperparameters, classify the documents from the different vector representations and apply a consensus algorithm on the different partitions. A constrained co-clustering of the co-occurrence matrix between terms and documents is then applied to maintain the consensus partitioning. This method is found to result in significantly better quality of document partitioning on various document corpora and provides the advantage of the interpretation offered by the co-clustering. Secondly, we present a method for evaluating co-clustering algorithms by exploiting vector representations of words called word embeddings. Word embeddings are vectors constructed using large volumes of text, one major characteristic of which is that two semantically close words have word embeddings close by a cosine distance. Our method makes it possible to measure the matching between the partition of the documents and the partition of the words, thus offering in a totally unsupervised setting a measure of the quality of the co-clustering. Thirdly, we are interested in recommending classified ads. We present a system that allows to recommend similar classified ads when consulting one. The descriptions of classified ads are often short, syntactically incorrect, and the use of synonyms makes it difficult for traditional systems to accurately measure semantic similarity. In addition, the high renewal rate of classified ads that are still valid (product not sold) implies choices that make it possible to have low computation time. Our method, simple to implement, responds to this use case and is again based on word embeddings. The use of these has advantages but also involves some difficulties: the creation of such vectors requires choosing the values of some parameters, and the difference between the corpus on which the word embeddings were built upstream. and the one on which they are used raises the problem of out-of-vocabulary words, which have no vector representation. To overcome these problems, we present an analysis of the impact of the different parameters on word embeddings as well as a study of the methods allowing to deal with the problem of out-of-vocabulary words
42
Kara, Sandra. "Unsupervised object discovery in images and video data." Electronic Thesis or Diss., université Paris-Saclay, 2025. http://www.theses.fr/2025UPASG019.
Full textAbstract:
Cette thèse explore les méthodes d'apprentissage auto-supervisé pour la localisation d'objets, communément appelées « Object Discovery ». La localisation d'objets dans les images et les vidéos est un élément essentiel des tâches de vision par ordinateur telles que la détection, la ré-identification, le suivi, etc. Les algorithmes supervisés actuels peuvent localiser (et classifier) les objets avec précision, mais ils sont coûteux en raison de la nécessité de données annotées. Le processus d'étiquetage est généralement répété pour chaque nouvelle donnée ou catégorie d'intérêt, limitant ainsi leur évolutivité. De plus, les approches sémantiquement spécialisées nécessitent une connaissance préalable des classes cibles, restreignant leur utilisation aux objets connus. La découverte d'objets vise à pallier ces limitations en étant plus générique. La première contribution de la thèse s'est concentrée sur la modalité image, en étudiant comment les caractéristiques des modèles transformers de vision auto-supervisés peuvent servir d'indices pour la découverte d'objets multiples. Afin de localiser les objets dans leur définition la plus large, nous avons étendu notre étude aux données vidéo, en exploitant les indices de mouvement et en ciblant la localisation d'objets capables de se déplacer. Nous avons introduit la modélisation de l'arrière-plan et la distillation de connaissances dans la découverte d'objets pour résoudre le problème de la sur-segmentation de l'arrière-plan dans les méthodes existantes, et pour réintégrer les objets statiques, améliorant ainsi de manière significative le rapport signal/bruit dans les prédictions. Reconnaissant les limites des données à modalité unique, nous avons incorporé des données 3D à travers un apprentissage par distillation de connaissances cross-modale. L'échange de connaissances entre les domaines 2D et 3D a permis d'améliorer l'alignement des régions d'objets entre les deux modalités, rendant possible l'utilisation de la cohérence multi-modale comme critère de confiance<br>This thesis explores self-supervised learning methods for object localization, commonly known as Object Discovery. Object localization in images and videos is an essential component of computer vision tasks such as detection, re-identification, tracking etc. Current supervised algorithms can localize (and classify) objects accurately but are costly due to the need for annotated data. The process of labeling is typically repeated for each new data or category of interest, limiting their scalability. Additionally, the semantically specialized approaches require prior knowledge of the target classes, restricting their use to known objects. Object Discovery aims to address these limitations by being more generic. The first contribution of this thesis focused on the image modality, investigating how features from self-supervised vision transformers can serve as cues for multi-object discovery. To localize objects in their broadest definition, we extended our focus to video data, leveraging motion cues and targeting the localization of objects that can move. We introduced background modeling and knowledge distillation in object discovery to tackle the background over-segmentation issue in existing object discovery methods and to reintegrate static objects, significantly improving the signal-to-noise ratio in predictions. Recognizing the limitations of single-modality data, we incorporated 3D data through a cross-modal distillation framework. The knowledge exchange between 2D and 3D domains improved alignment on object regions between the two modalities, enabling the use of multi-modal consistency as a confidence criterion
43
Prost, Vincent. "Sparse unsupervised learning for metagenomic data." Electronic Thesis or Diss., université Paris-Saclay, 2020. http://www.theses.fr/2020UPASL013.
Full textAbstract:
Les avancées technologiques dans le séquençage ADN haut débit ont permis à la métagénomique de considérablement se développer lors de la dernière décennie. Le séquencage des espèces directement dans leur milieu naturel a ouvert de nouveaux horizons dans de nombreux domaines de recherche. La réduction des coûts associée à l'augmentation du débit fait que de plus en plus d'études sont lancées actuellement.Dans cette thèse nous considérons deux problèmes ardus en métagénomique, à savoir le clustering de lectures brutes et l'inférence de réseaux microbiens. Pour résoudre ces problèmes, nous proposons de mettre en oeuvre des méthodes d'apprentissage non supervisées utilisant le principe de parcimonie, ce qui prend la forme concrète de problèmes d'optimisation avec une pénalisation de norme l1.Dans la première partie de la thèse, on considère le problème intermédiaire du clustering des séquences ADN dans des partitions biologiquement pertinentes (binning). La plupart des méthodes computationelles n'effectuent le binning qu'après une étape d'assemblage qui est génératrice d'erreurs (avec la création de contigs chimériques) et de pertes d'information. C'est pourquoi nous nous penchons sur le problème du binning sans assemblage préalable. Nous exploitons le signal de co-abondance des espèces au travers des échantillons mesuré via le comptage des k-mers (sous-séquences de taille k) longs. L'utilisation du Local Sensitive Hashing (LSH) permet de contenir, au coût d'une approximation, l'explosion combinatoire des k-mers possibles dans un espace de cardinal fixé. La première contribution de la thèse est de proposer l'application d'une factorisation en matrices non-négatives creuses (sparse NMF) sur la matrice de comptage des k-mers afin de conjointement extraire une information de variation d'abondance et d'effectuer le clustering des k-mers. Nous montrons d'abord le bien fondé de l'approche au niveau théorique. Puis, nous explorons dans l'état de l'art les méthodes de sparse NMF les mieux adaptées à notre problème. Les méthodes d'apprentissage de dictionnaire en ligne ont particulièrement retenu notre attention de par leur capacité à passer à l'échelle pour des jeux de données comportant un très grand nombre de points. La validation des méthodes de binning en métagénomique sur des données réelles étant difficile à cause de l'absence de vérité terrain, nous avons créé et utilisé plusieurs jeux de données synthétiques pour l'évaluation des différentes méthodes. Nous montrons que l'application de la sparse NMF améliore les méthodes de l'état de l'art pour le binning sur ces jeux de données. Des expérience sur des données métagénomiques réelles issus de 1135 échantillons de microbiotes intestinaux d'individus sains ont également été menées afin de montrer la pertinence de l'approche.Dans la seconde partie de la thèse, on considère les données métagénomiques après le profilage taxonomique, c'est à dire des donnés multivariées représentant les niveaux d'abondance des taxons au sein des échantillons. Les microbes vivant en communautés structurées par des interactions écologiques, il est important de pouvoir identifier ces interactions. Nous nous penchons donc sur le problème de l'inférence de réseau d'interactions microbiennes à partir des profils taxonomiques. Ce problème est souvent abordé dans le cadre théorique des modèles graphiques gaussiens (GGM), pour lequel il existe des algorithmes d'inférence puissants tel que le graphical lasso. Mais les méthodes statistiques existantes sont très limitées par l'aspect extrêmement creux des profils taxonomiques que l'on rencontre en métagénomique, notamment par la grande proportion de zéros dits biologiques (i.e. liés à l'absence réelle de taxons). Nous proposons un model log normal avec inflation de zéro visant à traiter ces zéros biologiques et nous montrons un gain de performance par rapport aux méthodes de l'état de l'art pour l'inférence de réseau d'interactions microbiennes<br>The development of massively parallel sequencing technologies enables to sequence DNA at high-throughput and low cost, fueling the rise of metagenomics which is the study of complex microbial communities sequenced in their natural environment.Metagenomic problems are usually computationally difficult and are further complicated by the massive amount of data involved.In this thesis we consider two different metagenomics problems: 1. raw reads binning and 2. microbial network inference from taxonomic abundance profiles. We address them using unsupervised machine learning methods leveraging the parsimony principle, typically involving l1 penalized log-likelihood maximization.The assembly of genomes from raw metagenomic datasets is a challenging task akin to assembling a mixture of large puzzles composed of billions or trillions of pieces (DNA sequences). In the first part of this thesis, we consider the related task of clustering sequences into biologically meaningful partitions (binning). Most of the existing computational tools perform binning after read assembly as a pre-processing, which is error-prone (yielding artifacts like chimeric contigs) and discards vast amounts of information in the form of unassembled reads (up to 50% for highly diverse metagenomes). This motivated us to try to address the raw read binning (without prior assembly) problem. We exploit the co-abundance of species across samples as discriminative signal. Abundance is usually measured via the number of occurrences of long k-mers (subsequences of size k). The use of Local Sensitive Hashing (LSH) allows us to contain, at the cost of some approximation, the combinatorial explosion of long k-mers indexing. The first contribution of this thesis is to propose a sparse Non-Negative Matrix factorization (NMF) of the samples x k-mers count matrix in order to extract abundance variation signals. We first show that using sparse NMF is well-grounded since data is a sparse linear mixture of non-negative components. Sparse NMF exploiting online dictionary learning algorithms retained our attention, including its decent behavior on largely asymmetric data matrices. The validation of metagenomic binning being difficult on real datasets, because of the absence of ground truth, we created and used several benchmarks for the different methods evaluated on. We illustrated that sparse NMF improves state of the art binning methods on those datasets. Experiments conducted on a real metagenomic cohort of 1135 human gut microbiota showed the relevance of the approach.In the second part of the thesis, we consider metagenomic data after taxonomic profiling: multivariate data representing abundances of taxa across samples. It is known that microbes live in communities structured by ecological interaction between the members of the community. We focus on the problem of the inference of microbial interaction networks from taxonomic profiles. This problem is frequently cast into the paradigm of Gaussian graphical models (GGMs) for which efficient structure inference algorithms are available, like the graphical lasso. Unfortunately, GGMs or variants thereof can not properly account for the extremely sparse patterns occurring in real-world metagenomic taxonomic profiles. In particular, structural zeros corresponding to true absences of biological signals fail to be properly handled by most statistical methods. We present in this part a zero-inflated log-normal graphical model specifically aimed at handling such "biological" zeros, and demonstrate significant performance gains over state-of-the-art statistical methods for the inference of microbial association networks, with most notable gains obtained when analyzing taxonomic profiles displaying sparsity levels on par with real-world metagenomic datasets
44
Bui, Thang Duc. "Efficient deterministic approximate Bayesian inference for Gaussian process models." Thesis, University of Cambridge, 2018. https://www.repository.cam.ac.uk/handle/1810/273833.
Full textAbstract:
Gaussian processes are powerful nonparametric distributions over continuous functions that have become a standard tool in modern probabilistic machine learning. However, the applicability of Gaussian processes in the large-data regime and in hierarchical probabilistic models is severely limited by analytic and computational intractabilities. It is, therefore, important to develop practical approximate inference and learning algorithms that can address these challenges. To this end, this dissertation provides a comprehensive and unifying perspective of pseudo-point based deterministic approximate Bayesian learning for a wide variety of Gaussian process models, which connects previously disparate literature, greatly extends them and allows new state-of-the-art approximations to emerge. We start by building a posterior approximation framework based on Power-Expectation Propagation for Gaussian process regression and classification. This framework relies on a structured approximate Gaussian process posterior based on a small number of pseudo-points, which is judiciously chosen to summarise the actual data and enable tractable and efficient inference and hyperparameter learning. Many existing sparse approximations are recovered as special cases of this framework, and can now be understood as performing approximate posterior inference using a common approximate posterior. Critically, extensive empirical evidence suggests that new approximation methods arisen from this unifying perspective outperform existing approaches in many real-world regression and classification tasks. We explore the extensions of this framework to Gaussian process state space models, Gaussian process latent variable models and deep Gaussian processes, which also unify many recently developed approximation schemes for these models. Several mean-field and structured approximate posterior families for the hidden variables in these models are studied. We also discuss several methods for approximate uncertainty propagation in recurrent and deep architectures based on Gaussian projection, linearisation, and simple Monte Carlo. The benefit of the unified inference and learning frameworks for these models are illustrated in a variety of real-world state-space modelling and regression tasks.
45
Henriksson, Maria P. "Human Rationality : Observing or Inferring Reality." Doctoral thesis, Uppsala universitet, Institutionen för psykologi, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-246315.
Full textAbstract:
This thesis investigates the boundary of human rationality and how psychological processes interact with underlying regularities in the environment and affect beliefs and achievement. Two common modes in everyday experiential learning, supervised and unsupervised learning were hypothesized to tap different ecological and epistemological approaches to human adaptation; the Brunswikian and the Gibsonian approach. In addition, they were expected to be differentially effective for achievement depending on underlying regularities in the task environment. The first approach assumes that people use top-down processes and learn from hypothesis testing and external feedback, while the latter assumes that people are receptive to environmental stimuli and learn from bottom-up processes, without mediating inferences and support from external feedback, only exploratory observations and actions. Study I investigates selective supervised learning and showed that biased beliefs arise when people store inferences about category members when information is partially absent. This constructivist coding of pseudo-exemplars in memory yields a conservative bias in the relative frequency of targeted category members when the information is constrained by the decision maker’s own selective sampling behavior, suggesting that niche picking and risk aversion contribute to conservatism or inertia in human belief systems. However, a liberal bias in the relative frequency of targeted category members is more likely when information is constrained by the external environment. This result suggests that highly exaggerated beliefs and risky behaviors may be more likely in environments where information is systematically manipulated, for example when positive examples are highlighted to convey a favorable image while negative examples are systematically withheld from the public eye. Study II provides support that the learning modes engage different processes. Supervised learning is more accurate in less complex linear task environments, while unsupervised learning is more accurate in complex nonlinear task environments. Study III provides further support for abstraction based on hypothesis testing in supervised learning, and abstraction based on receptive bottom-up processes in unsupervised learning that aimed to form ideal prototypes as highly valid reference points stored in memory. The studies support previous proposals that integrating the Brunswikian and the Gibsonian approach can broaden the scope of psychological research and scientific inquiry.
46
Simon, Etienne. "Deep Learning for Unsupervised Relation Extraction." Electronic Thesis or Diss., Sorbonne université, 2022. http://www.theses.fr/2022SORUS198.
Full textAbstract:
Détecter les relations exprimées dans un texte est un problème fondamental de la compréhension du langage naturel. Il constitue un pont entre deux approches historiquement distinctes de l'intelligence artificielle, celles à base de représentations symboliques et distribuées. Cependant, aborder ce problème sans supervision humaine pose plusieurs problèmes et les modèles non supervisés ont des difficultés à faire écho aux avancées des modèles supervisés. Cette thèse aborde deux lacunes des approches non supervisées : le problème de la régularisation des modèles discriminatifs et le problème d'exploitation des informations relationnelles à partir des structures des jeux de données. La première lacune découle de l'utilisation de réseaux neuronaux profonds. Ces modèles ont tendance à s'effondrer sans supervision. Pour éviter ce problème, nous introduisons deux fonctions de coût sur la distribution des relations pour contraindre le classifieur dans un état entraînable. La deuxième lacune découle du développement des approches au niveau des jeux de données. Nous montrons que les modèles non supervisés peuvent tirer parti d'informations issues de la structure des jeux de données, de manière encore plus décisive que les modèles supervisés. Nous exploitons ces structures en adaptant les méthodes non supervisées existantes pour capturer les informations topologiques à l'aide de réseaux convolutifs pour graphes. De plus, nous montrons que nous pouvons exploiter l'information mutuelle entre les données topologiques et linguistiques pour concevoir un nouveau paradigme d'entraînement pour l'extraction non supervisée de relations<br>Capturing concepts' interrelations is a fundamental of natural language understanding. It constitutes a bridge between two historically separate approaches of artificial intelligence: the use of symbolic and distributed representations. However, tackling this problem without human supervision poses several issues, and unsupervised models have difficulties echoing the expressive breakthroughs of supervised ones. This thesis addresses two supervision gaps we identified: the problem of regularization of sentence-level discriminative models and the problem of leveraging relational information from dataset-level structures. The first gap arises following the increased use of discriminative approaches, such as deep neural network classifiers, in the supervised setting. These models tend to collapse without supervision. To overcome this limitation, we introduce two relation distribution losses to constrain the relation classifier into a trainable state. The second gap arises from the development of dataset-level (aggregate) approaches. We show that unsupervised models can leverage a large amount of additional information from the structure of the dataset, even more so than supervised models. We close this gap by adapting existing unsupervised methods to capture topological information using graph convolutional networks. Furthermore, we show that we can exploit the mutual information between topological (dataset-level) and linguistic (sentence-level) information to design a new training paradigm for unsupervised relation extraction
47
Leto, Kevin. "Anomaly detection in HPC systems." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2019.
Find full textAbstract:
Nell’ambito dei supercomputer, l’attività di anomaly detection rappresenta un’ottima strategia per mantenere alte le performance del sistema (disponibilità ed affidabilità), consentendo di prevenire i guasti e di adattare l’attività di manutenzione alla salute del sistema stesso.
Il supercomputer esaminato da questa ricerca è chiamato MARCONI ed appartiene al CINECA, consorzio interuniversitario italiano con sede a Bologna. I dati estratti per l’analisi si riferiscono in particolar modo al nodo “r183c12s04”, ma per provare la generalità dell’approccio sono stati eseguiti ulteriori test anche su nodi differenti (seppur di minor portata).
L’approccio seguito sfrutta le potenzialità del machine learning, combinando addestramento non supervisionato e supervisionato. Un autoencoder viene addestrato in modo non supervisionato per ottenere una rappresentazione compressa (dimensionality reduction) dei dati grezzi estratti da un nodo del sistema. I dati compressi vengono poi forniti ad una rete neurale di 3 livelli (input, hidden, output) per effettuare una classificazione supervised tra stati normali e stati anomali.
Il nostro approccio si è rilevato molto promettente, raggiungendo livelli di accuracy, precision, recall e f1_score tutti superiori al 97% per il nodo principale. Mentre livelli più bassi, ma comunque molto positivi (mediamente superiori al 83%) sono stati riscontrati per gli altri nodi presi in considerazione. Le performance non perfette degli altri nodi sono sicuramente causate dal basso numero di esempi anomalie presenti nei dataset di riferimento.
48
Trivedi, Shubhendu. "A Graph Theoretic Clustering Algorithm based on the Regularity Lemma and Strategies to Exploit Clustering for Prediction." Digital WPI, 2012. https://digitalcommons.wpi.edu/etd-theses/573.
Full textAbstract:
The fact that clustering is perhaps the most used technique for exploratory data analysis is only a semaphore that underlines its fundamental importance. The general problem statement that broadly describes clustering as the identification and classification of patterns into coherent groups also implicitly indicates it's utility in other tasks such as supervised learning. In the past decade and a half there have been two developments that have altered the landscape of research in clustering: One is improved results by the increased use of graph theoretic techniques such as spectral clustering and the other is the study of clustering with respect to its relevance in semi-supervised learning i.e. using unlabeled data for improving prediction accuracies. In this work an attempt is made to make contributions to both these aspects. Thus our contributions are two-fold: First, we identify some general issues with the spectral clustering framework and while working towards a solution, we introduce a new algorithm which we call "Regularity Clustering" which makes an attempt to harness the power of the Szemeredi Regularity Lemma, a remarkable result from extremal graph theory for the task of clustering. Secondly, we investigate some practical and useful strategies for using clustering unlabeled data in boosting prediction accuracy. For all of these contributions we evaluate our methods against existing ones and also apply these ideas in a number of settings.
49
Balasubramanian, Krishnakumar. "Learning without labels and nonnegative tensor factorization." Thesis, Georgia Institute of Technology, 2010. http://hdl.handle.net/1853/33926.
Full textAbstract:
Supervised learning tasks like building a classifier, estimating the error rate of the
predictors, are typically performed with labeled data. In most cases, obtaining labeled data
is costly as it requires manual labeling. On the other hand, unlabeled data is available in
abundance. In this thesis, we discuss methods to perform supervised learning tasks with
no labeled data. We prove consistency of the proposed methods and demonstrate its applicability
with synthetic and real world experiments. In some cases, small quantities of labeled data maybe easily available and supplemented with large quantities of unlabeled data (semi-supervised learning). We derive the asymptotic efficiency of generative models for semi-supervised learning and quantify the effect of labeled and unlabeled data on the quality of the estimate. Another independent track of the thesis is efficient computational methods for nonnegative tensor factorization (NTF). NTF provides the user with rich modeling capabilities but it comes with an added computational cost. We provide a fast algorithm for performing NTF using a modified active set method called block principle pivoting method and demonstrate its applicability to social network analysis and text
mining.
50
Baur, Christoph [Verfasser], Nassir [Akademischer Betreuer] Navab, Nassir [Gutachter] Navab, and Ben [Gutachter] Glocker. "Anomaly Detection in Brain MRI: From Supervised to Unsupervised Deep Learning / Christoph Baur ; Gutachter: Nassir Navab, Ben Glocker ; Betreuer: Nassir Navab." München : Universitätsbibliothek der TU München, 2021. http://d-nb.info/1236343115/34.
Full text