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Dissertations / Theses on the topic 'Robust Classification'

Author: Grafiati

Published: 4 June 2021

Last updated: 26 July 2025

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1

Ge, Zongyuan. "Robust fine-grained image classification." Thesis, Queensland University of Technology, 2017. https://eprints.qut.edu.au/107700/1/Zongyuan_Ge_Thesis.pdf.

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This thesis tackles fine-grained image recognition, the task of sub-category or species classification. It explores general methods to improve fine-grained image classification including the use of generative models and deep convolutional neural networks leading to novel models such as a Mixture of deep convolution neural networks. This work led to 9 peer reviewed publications and a Best Paper Award.

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2

Podder, Mohua. "Robust genotype classification using dynamic variable selection." Thesis, University of British Columbia, 2008. http://hdl.handle.net/2429/1602.

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Single nucleotide polymorphisms (SNPs) are DNA sequence variations, occurring when a single nucleotide –A, T, C or G – is altered. Arguably, SNPs account for more than 90% of human genetic variation. Dr. Tebbutt's laboratory has developed a highly redundant SNP genotyping assay consisting of multiple probes with signals from multiple channels for a single SNP, based on arrayed primer extension (APEX). The strength of this platform is its unique redundancy having multiple probes for a single SNP. Using this microarray platform, we have developed fully-automated genotype calling algorithms based

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3

Chu, Wei 1966. "Auditory-based noise-robust audio classification algorithms." Thesis, McGill University, 2008. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=115863.

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The past decade has seen extensive research on audio classification algorithms which playa key role in multimedia applications, such as the retrieval of audio information from an audio or audiovisual database. However, the effect of background noise on the performance of classification has not been widely investigated. Motivated by the noise-suppression property of the early auditory (EA) model presented by Wang and Shamma, we seek in this thesis to further investigate this property and to develop improved algorithms for audio classification in the presence of background noise.<br>With respect

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4

Hu, Hong. "Accurate and robust algorithms for microarray data classification." University of Southern Queensland, Faculty of Sciences, 2008. http://eprints.usq.edu.au/archive/00006221/.

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[Abstract]Microarray data classification is used primarily to predict unseen data using a model built on categorized existing Microarray data. One of the major challenges is that Microarray data contains a large number of genes with a small number of samples. This high dimensionality problem has prevented many existing classification methods from directly dealing with this type of data. Moreover, the small number of samples increases the overfitting problem of Classification, as a result leading to lower accuracy classification performance. Another major challenge is that of the uncertainty of

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5

Tran, Brandon Vanhuy. "Building and using robust representations in image classification." Thesis, Massachusetts Institute of Technology, 2020. https://hdl.handle.net/1721.1/127912.

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Thesis: Ph. D., Massachusetts Institute of Technology, Department of Mathematics, May, 2020<br>Cataloged from the official PDF of thesis.<br>Includes bibliographical references (pages 115-131).<br>One of the major appeals of the deep learning paradigm is the ability to learn high-level feature representations of complex data. These learned representations obviate manual data pre-processing, and are versatile enough to generalize across tasks. However, they are not yet capable of fully capturing abstract, meaningful features of the data. For instance, the pervasiveness of adversarial examples--

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6

Siméoni, Oriane. "Robust image representation for classification, retrieval and object discovery." Thesis, Rennes 1, 2020. https://ged.univ-rennes1.fr/nuxeo/site/esupversions/415eb65b-d5f7-4be7-85e6-c2ecb2aba4dc.

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Les réseaux de neurones convolutifs (CNNs) ont été exploités avec succès pour la résolution de tâches dans le domaine de la vision par ordinateur tels que la classification, la segmentation d'image, la détection d'objets dans une image ou la recherche d'images dans une base de données. Typiquement, un réseau est entraîné spécifiquement pour une tâche et l'entraînement nécessite une très grande quantité d'images annotées. Dans cette thèse, nous proposons des solutions pour extraire le maximum d'information avec un minimum de supervision. D'abord, nous nous concentrons sur la tâche de classifica

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7

ALI, ARSLAN. "Deep learning techniques for biometric authentication and robust classification." Doctoral thesis, Politecnico di Torino, 2021. http://hdl.handle.net/11583/2910084.

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8

He, Jin. "Robust Mote-Scale Classification of Noisy Data via Machine Learning." The Ohio State University, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=osu1440413201.

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9

Carranza, Alarcón Yonatan Carlos. "Distributionally robust, skeptical inferences in supervised classification using imprecise probabilities." Thesis, Compiègne, 2020. http://www.theses.fr/2020COMP2567.

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Les décideurs sont souvent confrontés au défi de prendre des décisions précises, sans avoir aucune connaissance de la quantité d’incertitudes que celles-ci peuvent contenir, et en prenant le risque de commettre des erreurs dommageables, voire dramatiques. Dans de telles situations, où l’incertitude est plus élevée due à des informations imparfaites, il peut être plutôt utile de fournir des décisions prudentes, sous la forme d’un ensemble de solutions possibles, plus fiables. Ce travail se concentre donc sur la prise de décisions (ou inférences) sceptiques (ou prudentes) et robustes dans des pr

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10

Szekely, Robin [Verfasser]. "Robust and nonparametric classification of gene expression data / Robin Szekely." Ulm : Universität Ulm, 2021. http://d-nb.info/1237750725/34.

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11

Hossain, Md Tahmid. "Towards robust convolutional neural networks in challenging environments." Thesis, Federation University Australia, 2021. http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/181882.

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Image classification is one of the fundamental tasks in the field of computer vision. Although Artificial Neural Network (ANN) showed a lot of promise in this field, the lack of efficient computer hardware subdued its potential to a great extent. In the early 2000s, advances in hardware coupled with better network design saw the dramatic rise of Convolutional Neural Network (CNN). Deep CNNs pushed the State-of-The-Art (SOTA) in a number of vision tasks, including image classification, object detection, and segmentation. Presently, CNNs dominate these tasks. Although CNNs exhibit impressive cla

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12

Baumgartner, Dustin. "Global-Local Hybrid Classification Ensembles: Robust Performance with a Reduced Complexity." Connect to full text in OhioLINK ETD Center, 2009. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=toledo1241034194.

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Thesis (M.S.)--University of Toledo, 2009.<br>Typescript. "Submitted as partial fulfillment of the requirements for The Master of Science in Engineering." "A thesis entitled"--at head of title. Bibliography: leaves 158-164.

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13

Malady, Amy Colleen. "Cyclostationarity Feature-Based Detection and Classification." Thesis, Virginia Tech, 2011. http://hdl.handle.net/10919/32280.

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Cyclostationarity feature-based (C-FB) detection and classification is a large field of research that has promising applications to intelligent receiver design. Cyclostationarity FB classification and detection algorithms have been applied to a breadth of wireless communication signals â analog and digital alike. This thesis reports on an investigation of existing methods of extracting cyclostationarity features and then presents a novel robust solution that reduces SNR requirements, removes the pre-processing task of estimating occupied signal bandwidth, and can achieve classification rates

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14

Li, Jiuyong. "Optimal and Robust Rule Set Generation." Thesis, Griffith University, 2002. http://hdl.handle.net/10072/366394.

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The rapidly growing volume and complexity of modern databases makes the need for technologies to describe and summarise the information they contain increasingly important. Data mining is a process of extracting implicit, previously unknown and potentially useful patterns and relationships from data, and is widely used in industry and business applications. Rules characterise relationships among patterns in databases, and rule mining is one of the central tasks in data mining. There are fundamentally two categories of rules, namely association rules and classification rules. Traditionally, ass

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15

CAPPOZZO, ANDREA. "Robust model-based classification and clustering: advances in learning from contaminated datasets." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2020. http://hdl.handle.net/10281/262919.

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Al momento della stesura della tesi, ogni giorno viene raccolta una quantità sempre maggiore di dati, con un volume stimato che è destinato a raddoppiare ogni due anni. Grazie ai progressi tecnologici, i datasets stanno diventando enormi in termini di dimensioni e sostanzialmente più complessi in natura. Tuttavia, questa abbondanza di informazioni non elaborate ha un prezzo: misurazioni errate, errori di immissione dei dati, guasti dei sistemi di raccolta automatica e diverse altre cause possono in definitiva compromettere la qualità complessiva dei dati. I metodi robusti hanno un ruolo centra

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16

König, Sören, and Stefan Gumhold. "Robust Surface Reconstruction from Point Clouds." Technische Universität Dresden, 2013. https://tud.qucosa.de/id/qucosa%3A27391.

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The problem of generating a surface triangulation from a set of points with normal information arises in several mesh processing tasks like surface reconstruction or surface resampling. In this paper we present a surface triangulation approach which is based on local 2d delaunay triangulations in tangent space. Our contribution is the extension of this method to surfaces with sharp corners and creases. We demonstrate the robustness of the method on difficult meshing problems that include nearby sheets, self intersecting non manifold surfaces and noisy point samples.

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17

Ilea, Ioana. "Robust classifcation methods on the space of covariance matrices. : application to texture and polarimetric synthetic aperture radar image classification." Thesis, Bordeaux, 2017. http://www.theses.fr/2017BORD0006/document.

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Au cours de ces dernières années, les matrices de covariance ont montré leur intérêt dans de nombreuses applications en traitement du signal et de l'image.Les travaux présentés dans cette thèse se concentrent sur l'utilisation de ces matrices comme descripteurs pour la classification. Dans ce contexte, des algorithmes robustes de classification sont proposés en développant les aspects suivants.Tout d'abord, des estimateurs robustes de la matrice de covariance sont utilisés afin de réduire l'impact des observations aberrantes. Puis, les distributions Riemannienne Gaussienne et de Laplace, ainsi

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18

Khaki, Mohammad. "Robust Classification of Head Pose from Low Resolution Images Under Various Lighting Condition." Thesis, Université d'Ottawa / University of Ottawa, 2017. http://hdl.handle.net/10393/37060.

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Companies have long been interested in gauging the customer’s level of interest in their advertisements. By analyzing the gaze direction of individuals viewing a public advertisement, we can infer their level of engagement. Head pose detection allows us to deduce pertinent information about gaze direction. Using video sensors, machine learning methods, and image processing techniques, information pertaining to the head pose of people viewing advertisements can be automatically collected and mined. We propose a method for the coarse classification of head pose from low-resolution images in cro

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19

Kalluri, Hemanth Reddy. "FUSION OF SPECTRAL REFLECTANCE AND DERIVATIVE INFORMATION FOR ROBUST HYPERSPECTRAL LAND COVER CLASSIFICATION." MSSTATE, 2009. http://sun.library.msstate.edu/ETD-db/theses/available/etd-11062009-124333/.

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Developments in sensor technology have made high resolution hyperspectral remote sensing data available to the remote sensing analyst for ground cover classification and target recognition tasks. Further, with limited ground-truth data in many real-life operating scenarios, such hyperspectral classification systems often employ dimensionality reduction algorithms. In this thesis, the efficacy of spectral derivative features for hyperspectral analysis is studied. These studies are conducted within the context of both single and multiple classifier systems. Finally, a modification of existing cl

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20

Subramani, Palanisamy Harisubramanyabalaji. "Risk Assessment based Data Augmentation for Robust Image Classification : using Convolutional Neural Network." Thesis, Umeå universitet, Institutionen för tillämpad fysik och elektronik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-153049.

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Autonomous driving is increasingly popular among people and automotive industries in realizing their presence both in passenger and goods transportation. Safer autonomous navigation might be very challenging if there is a failure in sensing system. Among several sensing systems, image classification plays a major role in understanding the road signs and to regulate the vehicle control based on urban road rules. Hence, a robust classifier algorithm irrespective of camera position, view angles, environmental condition, different vehicle size & type (Car, Bus, Truck, etc.,) of an autonomous p

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21

Olvera, Zambrano Mauricio Michel. "Robust sound event detection." Electronic Thesis or Diss., Université de Lorraine, 2022. http://www.theses.fr/2022LORR0324.

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De l'industrie aux applications d'intérêt général, l'analyse automatique des scènes et événements sonores permet d'interpréter le flux continu de sons quotidiens. Une des principales dégradations rencontrées lors du passage des conditions de laboratoire au monde réel est due au fait que les scènes sonores ne sont pas composées d'événements isolés mais de plusieurs événements simultanés. Des différences entre les conditions d'apprentissage et de test surviennent aussi souvent en raison de facteurs extrinsèques, tels que le choix du matériel d'enregistrement et des positions des microphones, et

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22

Maximov, Ivan I., Farida Grinberg, and Nadim Jon Shah. "Robust estimator framework in diffusion tensor imaging." Diffusion fundamentals 18 (2013) 10, S. 1-6, 2013. https://ul.qucosa.de/id/qucosa%3A13717.

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Diffusion of water molecules in the human brain tissue has strong similarities with diffusion in porous media. It is affected by different factors such as restrictions and compartmentalization, interaction with membrane walls, strong anisotropy imposed by cellular microstructure, etc. However, multiple artefacts abound in in vivo measurements either from subject motions, such as cardiac pulsation, bulk head motion, respiratory motion, and involuntary tics and tremor, or hardware related problems, such as table vibrations, etc. All these artefacts can substantially degrade the resulting images

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23

Medasani, Swarup. "Robust algorithms for mixture decomposition with application to classification, boundary description, and image retrieval /." free to MU campus, to others for purchase, 1998. http://wwwlib.umi.com/cr/mo/fullcit?p9904860.

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24

König, Sören, and Stefan Gumhold. "Robust Surface Triangulation of Points with Normal Information." Technische Universität Dresden, 2013. https://tud.qucosa.de/id/qucosa%3A27382.

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The problem of generating a surface triangulation from a set of points with normal information arises in several mesh processing tasks like surface reconstruction or surface resampling. In this paper we present a surface triangulation approach which is based on local 2d delaunay triangulations in tangent space. Our contribution is the extension of this method to surfaces with sharp corners and creases. We demonstrate the robustness of the method on difficult meshing problems that include nearby sheets, self intersecting non manifold surfaces and noisy point samples.

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25

Herrmann, Kai, Hannes Voigt, Jonas Rausch, Andreas Behrend, and Wolfgang Lehner. "Robust and simple database evolution." Springer, 2017. https://tud.qucosa.de/id/qucosa%3A75553.

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Software developers adapt to the fast-moving nature of software systems with agile development techniques. However, database developers lack the tools and concepts to keep the pace. Whenever the current database schema is evolved, the already existing data needs to be evolved as well. This is usually realized with manually written SQL scripts, which is error-prone and explains significant costs in software projects. A promising solution are declarative database evolution languages, which couple both schema and data evolution into intuitive operations. Existing database evolution languages focu

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26

Prasad, Saurabh. "MULTI-CLASSIFIERS AND DECISION FUSION FOR ROBUST STATISTICAL PATTERN RECOGNITION WITH APPLICATIONS TO HYPERSPECTRAL CLASSIFICATION." MSSTATE, 2008. http://sun.library.msstate.edu/ETD-db/theses/available/etd-11052008-125134/.

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In this dissertation, a multi-classifier, decision fusion framework is proposed for robust classification of high dimensional data in small-sample-size conditions. Such datasets present two key challenges. (1) The high dimensional feature spaces compromise the classifiers generalization ability in that the classifier tends to over-fit decision boundaries to the training data. This phenomenon is commonly known as the Hughes phenomenon in the pattern classification community. (2) The small-sample-size of the training data results in ill-conditioned estimates of its statistics. Most classifiers r

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27

La, Barre Kathryn. "Faceted navigation and browsing features in new OPACs: A more robust solution to problems of information seekers? (extended abstract)." dLIST, 2007. http://hdl.handle.net/10150/106157.

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In November, 2005, James Billington, the Librarian of Congress, proposed the creation of a “World Digital Library” of manuscripts and multimedia materials in order to “bring together online, rare and unique cultural materials.” Google became the first private sector partner for this project with a pledge of 3 million dollars (http://www.loc.gov/today/pr/2005/05- 250.html). One month later, the Bibliographic Services Task Force of the University of California Libraries released a report: Rethinking how we provide bibliographic services for the University of California. (Bibliographic Services T

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28

Mathieu, Timothée. "M-estimation and Median of Means applied to statistical learning Robust classification via MOM minimization MONK – outlier-robust mean embedding estimation by median-of-means Excess risk bounds in robust empirical risk minimization." Thesis, université Paris-Saclay, 2021. http://www.theses.fr/2021UPASM002.

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Le principal objectif de cette thèse est d'étudier des méthodes d'apprentissage statistique robuste. Traditionnellement, en statistique nous utilisons des modèles ou des hypothèses simplificatrices qui nous permettent de représenter le monde réel tout en sachant l'analyser convenablement. Cependant, certaines déviations des hypothèses peuvent fortement perturber l'analyse statistique d'une base de données. Par statistiques robuste, nous entendons ici des méthodes pouvant gérer d'une part des données dites anormales (erreur de capteur, erreur humaine) mais aussi des données de nature très varia

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Sadeghzadeh, Seyedehsaloumeh. "Optimal Data-driven Methods for Subject Classification in Public Health Screening." Diss., Virginia Tech, 2019. http://hdl.handle.net/10919/101611.

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Biomarker testing, wherein the concentration of a biochemical marker is measured to predict the presence or absence of a certain binary characteristic (e.g., a disease) in a subject, is an essential component of public health screening. For many diseases, the concentration of disease-related biomarkers may exhibit a wide range, particularly among the disease positive subjects, in part due to variations caused by external and/or subject-specific factors. Further, a subject's actual biomarker concentration is not directly observable by the decision maker (e.g., the tester), who has access only t

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30

Stanislas, Leo. "Detecting airborne particles in sensor data with deep learning for robust robot perception in adverse environments." Thesis, Queensland University of Technology, 2020. https://eprints.qut.edu.au/200382/1/Leo_Stanislas_Thesis.pdf.

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This thesis presents a novel method to detect airborne particles such as dust, fog, or smoke, in the data from LiDAR sensors and stereo cameras, two types of perception sensors commonly used in robotics. The proposed approach uses deep learning classification and stochastic data fusion to detect and correctly interpret sensor data points impacted by airborne particles. The work from this thesis will enable robots to reliably perform complex tasks in challenging and unpredictable environments such as mines, agricultural fields, or roads, including in adverse weather conditions.

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31

Tran, Thi Quynh Nhi. "Robust and comprehensive joint image-text representations." Thesis, Paris, CNAM, 2017. http://www.theses.fr/2017CNAM1096/document.

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La présente thèse étudie la modélisation conjointe des contenus visuels et textuels extraits à partir des documents multimédias pour résoudre les problèmes intermodaux. Ces tâches exigent la capacité de ``traduire'' l'information d'une modalité vers une autre. Un espace de représentation commun, par exemple obtenu par l'Analyse Canonique des Corrélation ou son extension kernelisée est une solution généralement adoptée. Sur cet espace, images et texte peuvent être représentés par des vecteurs de même type sur lesquels la comparaison intermodale peut se faire directement.Néanmoins, un tel espace

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32

Zens, Gregor. "Bayesian shrinkage in mixture-of-experts models: identifying robust determinants of class membership." Springer, 2019. http://dx.doi.org/10.1007/s11634-019-00353-y.

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A method for implicit variable selection in mixture-of-experts frameworks is proposed. We introduce a prior structure where information is taken from a set of independent covariates. Robust class membership predictors are identified using a normal gamma prior. The resulting model setup is used in a finite mixture of Bernoulli distributions to find homogenous clusters of women in Mozambique based on their information sources on HIV. Fully Bayesian inference is carried out via the implementation of a Gibbs sampler.

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33

Rätsch, Gunnar. "Robust boosting via convex optimization." Phd thesis, Universität Potsdam, 2001. http://opus.kobv.de/ubp/volltexte/2005/39/.

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In dieser Arbeit werden statistische Lernprobleme betrachtet. Lernmaschinen extrahieren Informationen aus einer gegebenen Menge von Trainingsmustern, so daß sie in der Lage sind, Eigenschaften von bisher ungesehenen Mustern - z.B. eine Klassenzugehörigkeit - vorherzusagen. Wir betrachten den Fall, bei dem die resultierende Klassifikations- oder Regressionsregel aus einfachen Regeln - den Basishypothesen - zusammengesetzt ist. Die sogenannten Boosting Algorithmen erzeugen iterativ eine gewichtete Summe von Basishypothesen, die gut auf ungesehenen Mustern vorhersagen. <br /> Die Arbeit behandelt

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34

Black, James Noel. "Development of a Support-Vector-Machine-based Supervised Learning Algorithm for Land Cover Classification Using Polarimetric SAR Imagery." Thesis, Virginia Tech, 2018. http://hdl.handle.net/10919/85391.

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Land cover classification using Synthetic Aperture Radar (SAR) data has been a topic of great interest in recent literature. Food commodities output prediction through crop identification, environmental monitoring, and forest regrowth tracking are some of the many problems that can be aided by land cover classification methods. The need for fast and automated classification methods is apparent in a variety of applications involving vast amounts of SAR data. One fundamental step in any supervised learning classification algorithm is the selection and/or extraction of features present in the

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35

Yapanel, Umit. "Acoustic modeling and speaker normalization strategies with application to robust in-vehicle speech recognition and dialect classification." Diss., Connect to online resource, 2005. http://wwwlib.umi.com/cr/colorado/fullcit?p3190395.

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36

Wang, Wenjuan. "Optimization algorithms for SVM classification : Applications to geometrical chromosome analysis." Thesis, Toulouse 3, 2016. http://www.theses.fr/2016TOU30111/document.

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Le génome est très organisé au sein du noyau cellulaire. Cette organisation et plus spécifiquement la localisation et la dynamique des gènes et chromosomes contribuent à l'expression génétique et la différenciation des cellules que ce soit dans le cas de pathologies ou non. L'exploration de cette organisation pourrait dans le futur aider à diagnostiquer et identifier de nouvelles cibles thérapeutiques. La conformation des chromosomes peut être analysée grâce au marquage ADN sur plusieurs sites et aux mesures de distances entre ces différents marquages fluorescents. Dans ce contexte, l'organisa

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Tran, Thi Quynh Nhi. "Robust and comprehensive joint image-text representations." Electronic Thesis or Diss., Paris, CNAM, 2017. http://www.theses.fr/2017CNAM1096.

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La présente thèse étudie la modélisation conjointe des contenus visuels et textuels extraits à partir des documents multimédias pour résoudre les problèmes intermodaux. Ces tâches exigent la capacité de ``traduire'' l'information d'une modalité vers une autre. Un espace de représentation commun, par exemple obtenu par l'Analyse Canonique des Corrélation ou son extension kernelisée est une solution généralement adoptée. Sur cet espace, images et texte peuvent être représentés par des vecteurs de même type sur lesquels la comparaison intermodale peut se faire directement.Néanmoins, un tel espace

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Cubillos, Mesías Macarena Yasmara. "Robust Treatment Planning and Robustness Evaluation for Proton Therapy of Head and Neck Cancer." Technische Universität Dresden, 2019. https://tud.qucosa.de/id/qucosa%3A73383.

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Intensity modulated proton therapy (IMPT) in head and neck squamous cell carcinoma (HNSCC) offers superior advantages over conventional photon therapy, by generating high conformal doses to the target volume and improved sparing of the organ at risks (OARs). Besides, robust treatment planning approaches, which account for uncertainties directly into the plan optimization process, are able to generate high quality plans robust against uncertainties compared to a PTV margin expansion approach. During radiation treatment, patients are prone to present anatomical variations during the treatment co

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Lehmann, Rüdiger. "A universal and robust computation procedure for geometric observations." Hochschule für Technik und Wirtschaft, 2017. https://htw-dresden.qucosa.de/id/qucosa%3A31843.

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This contribution describes an automatic and robust method, which can be applied to all classical geodetic computation problems. Starting from given input quantities (e.g. coordinates of known points, observations) computation opportunities for all other relevant quantities are found. For redundant input quantities there exists a multitude of different computation opportunities from different minimal subsets of input quantities, which are all found automatically, and their results are computed and compared. If the computation is non-unique, but only a finite number of solutions exist, then all

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Kato, Jien, Toyohide Watanabe, Sébastien Joga, et al. "An HMM/MRF-based stochastic framework for robust vehicle tracking." IEEE, 2004. http://hdl.handle.net/2237/6743.

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41

Zouhri, Wahb. "Quality prediction/classification of a production system under uncertainty based on Support Vector Machine." Thesis, Paris, HESAM, 2020. http://www.theses.fr/2020HESAE058.

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Avec l'émergence des techniques d’IoT, les industries manufacturières adoptent de nouvelles technologies d'analyse de données afin d’améliorer la qualité de leurs systèmes de production. Les méthodes de classification offrent diverses solutions aux problèmes de management de la qualité, comme la détection des défauts et la prédiction de la conformité. Cependant, les données de production sont entachées d’incertitudes qui affectent les performances de ces méthodes. Ces travaux visent à étudier l'impact des incertitudes de mesure sur les performances des machines à vecteurs supports (SVM). Deux

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Rodríguez, López Pau. "Towards robust neural models for fine-grained image recognition." Doctoral thesis, Universitat Autònoma de Barcelona, 2019. http://hdl.handle.net/10803/667196.

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Reconèixer i identificar diverses subcategories en el nostre entorn és una activitat crucial a les nostres vides. Reconèixer un amic, trobar cert bacteri en imatges de microscopi, o descobrir un nou tipus de galàxia en són només alguns exemples. Malgrat això, el reconeixement de subcategories en imatges encara és una tasca costosa en el camp de la visió per computador, ja que les diferències entre dues imatges de la mateixa subcategoria eclipsen els detalls que distingeixen dues subcategories diferents. En aquest tipus de problema, en què la distinció entre categories radica en diferènc

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Willot, Hénoïk. "Certified explanations of robust models." Electronic Thesis or Diss., Compiègne, 2024. http://www.theses.fr/2024COMP2812.

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Avec l'utilisation croissante des systèmes d'aide à la décision, automatisés ou semi-automatisés, en intelligence artificielle se crée le besoin de les rendre fiables et transparents pour un utilisateur final. Tandis que le rôle des méthodes d'explicabilité est généralement d'augmenter la transparence, la fiabilité peut être obtenue en fournissant des explications certifiées, dans le sens qu'elles sont garanties d'être vraies, et en considérant des modèles robustes qui peuvent s'abstenir quand l'information disponible est trop insuffisante, plutôt que de forcer une décision dans l'unique but d

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Siddiqui, Abdul Jabbar. "A Robust Vehicle Make and Model Recognition System for ITS Applications." Thesis, Université d'Ottawa / University of Ottawa, 2015. http://hdl.handle.net/10393/33124.

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A real-time Vehicle Make and Model Recognition (VMMR) system is a significant component of security applications in Intelligent Transportation Systems (ITS). A highly accurate and real-time VMMR system significantly reduces the overhead cost of resources otherwise required. In this thesis, we present a VMMR system that provides very high classification rates and is robust to challenges like low illumination, occlusions, partial and non-frontal views. These challenges are encountered in realistic environments and high security areas like parking lots and public spaces (e.g., malls, stadiums, an

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Koch, R. J., S. Fryska, M. Ostler, et al. "Robust phonon-plasmon coupling in quasi-freestanding graphene on silicon carbide." Technische Universität Chemnitz, 2016. https://monarch.qucosa.de/id/qucosa%3A21187.

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Using inelastic electron scattering in combination with dielectric theory simulations on differently prepared graphene layers on silicon carbide we demonstrate that the coupling between the 2D plasmon of graphene and the surface optical phonon of the substrate cannot be quenched by modifcation of the interface via intercalation. The intercalation rather provides additional modes like, e.g., the silicon-hydrogen stretch mode in the case of hydrogen intercalation or the silicon-oxygen vibrations for water intercalation that couple to the 2D plasmons of graphene. Furthermore, in the case of bilay

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Terzi, Matteo. "Learning interpretable representations for classification, anomaly detection, human gesture and action recognition." Doctoral thesis, Università degli studi di Padova, 2019. http://hdl.handle.net/11577/3423183.

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The goal of this thesis is to provide algorithms and models for classification, gesture recognition and anomaly detection with a partial focus on human activity. In applications where humans are involved, it is of paramount importance to provide robust and understandable algorithms and models. A way to accomplish this requirement is to use relatively simple and robust approaches, especially when devices are resource-constrained. The second approach, when a large amount of data is present, is to adopt complex algorithms and models and make them robust and interpretable from a human-like point

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Strange, Andrew Darren. "Robust thin layer coal thickness estimation using ground penetrating radar." Thesis, Queensland University of Technology, 2007. https://eprints.qut.edu.au/16356/1/Andrew_Strange_Thesis.pdf.

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One of the most significant goals in coal mining technology research is the automation of underground coal mining machinery. A current challenge with automating underground coal mining machinery is measuring and maintaining a coal mining horizon. The coal mining horizon is the horizontal path the machinery follows through the undulating coal seam during the mining operation. A typical mining practice is to leave a thin remnant of coal unmined in order to maintain geological stability of the cutting face. If the remnant layer is too thick, resources are wasted as the unmined coal is permanently

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Strange, Andrew Darren. "Robust thin layer coal thickness estimation using ground penetrating radar." Queensland University of Technology, 2007. http://eprints.qut.edu.au/16356/.

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One of the most significant goals in coal mining technology research is the automation of underground coal mining machinery. A current challenge with automating underground coal mining machinery is measuring and maintaining a coal mining horizon. The coal mining horizon is the horizontal path the machinery follows through the undulating coal seam during the mining operation. A typical mining practice is to leave a thin remnant of coal unmined in order to maintain geological stability of the cutting face. If the remnant layer is too thick, resources are wasted as the unmined coal is permanently

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Lehmann, Rüdiger. "Geodätische Fehlerrechnung mit der skalenkontaminierten Normalverteilung." Hochschule für Technik und Wirtschaft Dresden, 2012. https://htw-dresden.qucosa.de/id/qucosa%3A23282.

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Geodätische Messabweichungen werden oft gut durch Wahrscheinlichkeitsverteilungen beschrieben, die steilgipfliger als die Gaußsche Normalverteilung sind. Das gilt besonders, wenn grobe Messabweichungen nicht völlig ausgeschlossen werden können. Neben einigen in der Geodäsie bisher verwendeten Verteilungen (verallgemeinerte Normalverteilung, Hubers Verteilung) diskutieren wir hier die skalenkontaminierte Normalverteilung, die für die praktische Rechnung einige Vorteile bietet.<br>Geodetic measurement errors are frequently well described by probability distributions, which are more peak-shaped t

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Yerlikaya, Fatma. "A New Contribution To Nonlinear Robust Regression And Classification With Mars And Its Applications To Data Mining For Quality Control In Manufacturing." Master's thesis, METU, 2008. http://etd.lib.metu.edu.tr/upload/3/12610037/index.pdf.

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Multivariate adaptive regression spline (MARS) denotes a modern methodology from statistical learning which is very important in both classification and regression, with an increasing number of applications in many areas of science, economy and technology. MARS is very useful for high dimensional problems and shows a great promise for fitting nonlinear multivariate functions. MARS technique does not impose any particular class of relationship between the predictor variables and outcome variable of interest. In other words, a special advantage of MARS lies in its ability to estimate the contri

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