Relevant bibliographies by topics / Self-supervised learning (artificial intelligence) / Dissertations / Theses
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Dissertations / Theses on the topic 'Self-supervised learning (artificial intelligence)'

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Published: 7 July 2024
Last updated: 31 July 2025

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1

Denize, Julien. "Self-supervised representation learning and applications to image and video analysis." Electronic Thesis or Diss., Normandie, 2023. http://www.theses.fr/2023NORMIR37.

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Dans cette thèse, nous développons des approches d'apprentissage auto-supervisé pour l'analyse d'images et de vidéos. L'apprentissage de représentation auto-supervisé permet de pré-entraîner les réseaux neuronaux à apprendre des concepts généraux sans annotations avant de les spécialiser plus rapidement à effectuer des tâches, et avec peu d'annotations. Nous présentons trois contributions à l'apprentissage auto-supervisé de représentations d'images et de vidéos. Premièrement, nous introduisons le paradigme théorique de l'apprentissage contrastif doux et sa mise en œuvre pratique appelée Estima
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Nett, Ryan. "Dataset and Evaluation of Self-Supervised Learning for Panoramic Depth Estimation." DigitalCommons@CalPoly, 2020. https://digitalcommons.calpoly.edu/theses/2234.

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Depth detection is a very common computer vision problem. It shows up primarily in robotics, automation, or 3D visualization domains, as it is essential for converting images to point clouds. One of the poster child applications is self driving cars. Currently, the best methods for depth detection are either very expensive, like LIDAR, or require precise calibration, like stereo cameras. These costs have given rise to attempts to detect depth from a monocular camera (a single camera). While this is possible, it is harder than LIDAR or stereo methods since depth can't be measured from monocular
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3

Stanescu, Ana. "Semi-supervised learning for biological sequence classification." Diss., Kansas State University, 2015. http://hdl.handle.net/2097/35810.

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Doctor of Philosophy<br>Department of Computing and Information Sciences<br>Doina Caragea<br>Successful advances in biochemical technologies have led to inexpensive, time-efficient production of massive volumes of data, DNA and protein sequences. As a result, numerous computational methods for genome annotation have emerged, including machine learning and statistical analysis approaches that practically and efficiently analyze and interpret data. Traditional machine learning approaches to genome annotation typically rely on large amounts of labeled data in order to build quality classifiers. T
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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.

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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 ti
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Halpern, Yonatan. "Semi-Supervised Learning for Electronic Phenotyping in Support of Precision Medicine." Thesis, New York University, 2016. http://pqdtopen.proquest.com/#viewpdf?dispub=10192124.

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<p> Medical informatics plays an important role in precision medicine, delivering the right information to the right person, at the right time. With the introduction and widespread adoption of electronic medical records, in the United States and world-wide, there is now a tremendous amount of health data available for analysis.</p><p> Electronic record phenotyping refers to the task of determining, from an electronic medical record entry, a concise descriptor of the patient, comprising of their medical history, current problems, presentation, etc. In inferring such a phenotype descriptor fro
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Taylor, Farrell R. "Evaluation of Supervised Machine Learning for Classifying Video Traffic." NSUWorks, 2016. http://nsuworks.nova.edu/gscis_etd/972.

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Operational deployment of machine learning based classifiers in real-world networks has become an important area of research to support automated real-time quality of service decisions by Internet service providers (ISPs) and more generally, network administrators. As the Internet has evolved, multimedia applications, such as voice over Internet protocol (VoIP), gaming, and video streaming, have become commonplace. These traffic types are sensitive to network perturbations, e.g. jitter and delay. Automated quality of service (QoS) capabilities offer a degree of relief by prioritizing network t
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Coursey, Kino High. "An Approach Towards Self-Supervised Classification Using Cyc." Thesis, University of North Texas, 2006. https://digital.library.unt.edu/ark:/67531/metadc5470/.

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Due to the long duration required to perform manual knowledge entry by human knowledge engineers it is desirable to find methods to automatically acquire knowledge about the world by accessing online information. In this work I examine using the Cyc ontology to guide the creation of Naïve Bayes classifiers to provide knowledge about items described in Wikipedia articles. Given an initial set of Wikipedia articles the system uses the ontology to create positive and negative training sets for the classifiers in each category. The order in which classifiers are generated and used to test articles
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Livi, Federico. "Supervised Learning with Graph Structured Data for Transprecision Computing." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2019. http://amslaurea.unibo.it/19714/.

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Nell'era dell'Internet of things, dei Big Data e dell'industria 4.0, la crescente richiesta di risorse e strumenti atti ad elaborare la grande quantità di dati e di informazioni disponibili in ogni momento, ha posto l'attenzione su problemi oramai non più trascurabili inerenti al consumo di energia e ai costi che ne derivano. Si tratta del cosiddetto powerwall, ovvero della difficoltà fisica dei macchinari di sostenere il consumo di potenza necessario per il processamento di moli di dati sempre più grandi e per l'esecuzione di task sempre più sofisticati. Tra le nuove tecniche che si sono affe
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Rossi, Alex. "Self-supervised information retrieval: a novel approach based on Deep Metric Learning and Neural Language Models." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021.

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Most of the existing open-source search engines, utilize keyword or tf-idf based techniques to find relevant documents and web pages relative to an input query. Although these methods, with the help of a page rank or knowledge graphs, proved to be effective in some cases, they often fail to retrieve relevant instances for more complicated queries that would require a semantic understanding to be exploited. In this Thesis, a self-supervised information retrieval system based on transformers is employed to build a semantic search engine over the library of Gruppo Maggioli company. Semantic sear
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Stroulia, Eleni. "Failure-driven learning as model-based self-redesign." Diss., Georgia Institute of Technology, 1994. http://hdl.handle.net/1853/8291.

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Watkins, Andrew B. "AIRS: a resource limited artificial immune classifier." Master's thesis, Mississippi State : Mississippi State University, 2001. http://library.msstate.edu/etd/show.asp?etd=etd-11052001-102048.

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Apprey-Hermann, Joseph Kwame. "Evaluating The Predictability of Pseudo-Random Number Generators Using Supervised Machine Learning Algorithms." Youngstown State University / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ysu1588805461290138.

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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.

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Othmani-Guibourg, Mehdi. "Supervised learning for distribution of centralised multiagent patrolling strategies." Electronic Thesis or Diss., Sorbonne université, 2019. http://www.theses.fr/2019SORUS534.

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Depuis presque deux décennies, la tâche de la patrouille a fait l'objet d'une attention toute particulière de la part de la communauté multi-agent. La patrouille multi-agent consiste à modéliser comme un système multi-agent une tâche de patrouille à optimiser. Cette optimisation revient à répartir dans l'espace et le temps les agents patrouilleurs sur la zone à surveiller, cela le plus efficacement possible; un tel problème constitue par là même un problème de décision. Un large éventail d'algorithmes basés sur des stratégies d’agent réactives, cognitives, d’apprentissage par renforcement, cen
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Mao, Yi. "Domain knowledge, uncertainty, and parameter constraints." Diss., Georgia Institute of Technology, 2010. http://hdl.handle.net/1853/37295.

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Calderon-Vilca, Hugo D., William I. Wun-Rafael, and Roberto Miranda-Loarte. "Simulation of suicide tendency by using machine learning." IEEE Computer Society, 2018. http://hdl.handle.net/10757/624720.

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El texto completo de este trabajo no está disponible en el Repositorio Académico UPC por restricciones de la casa editorial donde ha sido publicado.<br>Suicide is one of the most distinguished causes of death on the news worldwide. There are several factors and variables that can lead a person to commit this act, for example, stress, self-esteem, depression, among others. The causes and profiles of suicide cases are not revealed in detail by the competent institutions. We propose a simulation with a systematically generated dataset; such data reflect the adolescent population with suicidal ten
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Charnay, Clément. "Enhancing supervised learning with complex aggregate features and context sensitivity." Thesis, Strasbourg, 2016. http://www.theses.fr/2016STRAD025/document.

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Dans cette thèse, nous étudions l'adaptation de modèles en apprentissage supervisé. Nous adaptons des algorithmes d'apprentissage existants à une représentation relationnelle. Puis, nous adaptons des modèles de prédiction aux changements de contexte.En représentation relationnelle, les données sont modélisées par plusieurs entités liées par des relations. Nous tirons parti de ces relations avec des agrégats complexes. Nous proposons des heuristiques d'optimisation stochastique pour inclure des agrégats complexes dans des arbres de décisions relationnels et des forêts, et les évaluons sur des j
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Buchi, Baptiste. "Learning system for self-reconfiguration of micro-robot networks." Electronic Thesis or Diss., Bourgogne Franche-Comté, 2023. http://www.theses.fr/2023UBFCA017.

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Le problème d'auto-reconfiguration des réseaux de micro-robots est l'un des défis majeurs de la robotique modulaire. Un ensemble de micro-robots reliés par des liens électromagnétiques ou mécaniques se réorganisent afin d'atteindre des formes cibles données. Le problème d'auto-reconfiguration est un problème complexe pour trois raisons. Premièrement, le nombre de configurations distinctes d'un réseau de robots modulaires est très élevé. Deuxièmement, comme les modules sont libres de se mouvoir indépendamment les uns des autres, à partir de chaque configuration il est possible d'atteindre un no
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Dhyani, Dushyanta Dhyani. "Boosting Supervised Neural Relation Extraction with Distant Supervision." The Ohio State University, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=osu1524095334803486.

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Balasubramanian, Krishnakumar. "Learning without labels and nonnegative tensor factorization." Thesis, Georgia Institute of Technology, 2010. http://hdl.handle.net/1853/33926.

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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 qu
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Minakshi, Mona. "A Machine Learning Framework to Classify Mosquito Species from Smart-phone Images." Scholar Commons, 2018. https://scholarcommons.usf.edu/etd/7340.

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Mosquito borne diseases have been a constant scourge across the globe resulting in numerous diseases with debilitating consequences, and also death. To derive trends on population of mosquitoes in an area, trained personnel lay traps, and after collecting trapped specimens, they spend hours under a microscope to inspect each specimen for identifying the actual species and logging it. This is vital, because multiple species of mosquitoes can reside in any area, and the vectors that some of them carry are not the same ones carried by others. The speci
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Yu, Chen-Ping. "Computational model of MST neuron receptive field and interaction effect for the perception of self-motion /." Online version of thesis, 2008. http://hdl.handle.net/1850/9588.

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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.

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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.

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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 uns
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Leoni, Cristian. "Interpretation of Dimensionality Reduction with Supervised Proxies of User-defined Labels." Thesis, Linnéuniversitetet, Institutionen för datavetenskap och medieteknik (DM), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-105622.

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Research on Machine learning (ML) explainability has received a lot of focus in recent times. The interest, however, mostly focused on supervised models, while other ML fields have not had the same level of attention. Despite its usefulness in a variety of different fields, unsupervised learning explainability is still an open issue. In this paper, we present a Visual Analytics framework based on eXplainable AI (XAI) methods to support the interpretation of Dimensionality reduction methods. The framework provides the user with an interactive and iterative process to investigate and explain use
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Cao, Xi Hang. "On Leveraging Representation Learning Techniques for Data Analytics in Biomedical Informatics." Diss., Temple University Libraries, 2019. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/586006.

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Computer and Information Science<br>Ph.D.<br>Representation Learning is ubiquitous in state-of-the-art machine learning workflow, including data exploration/visualization, data preprocessing, data model learning, and model interpretations. However, the majority of the newly proposed Representation Learning methods are more suitable for problems with a large amount of data. Applying these methods to problems with a limited amount of data may lead to unsatisfactory performance. Therefore, there is a need for developing Representation Learning methods which are tailored for problems with ``small
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Budnyk, Ivan. "Contribution to the Study and Implementation of Intelligent Modular Self-organizing Systems." Phd thesis, Université Paris-Est, 2009. http://tel.archives-ouvertes.fr/tel-00481367.

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Les problèmes de la classification ont reçu une attention considérable dans des différents champs d'ingénierie comme traitement des images biomédicales, identification a partir de la voix, reconnaissance d'empreinte digitale etc. Les techniques d'intelligence artificielles, incluant les réseaux de neurones artificiels, permettent de traiter des problèmes de ce type. En particulier, les problèmes rencontrés nécessitent la manipulation de bases de données de tailles très importantes. Des structures de traitement adaptatives et exploitant des ensembles de classificateurs sont utilisées. Dans cett
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Kim, Seungyeon. "Novel document representations based on labels and sequential information." Diss., Georgia Institute of Technology, 2015. http://hdl.handle.net/1853/53946.

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A wide variety of text analysis applications are based on statistical machine learning techniques. The success of those applications is critically affected by how we represent a document. Learning an efficient document representation has two major challenges: sparsity and sequentiality. The sparsity often causes high estimation error, and text's sequential nature, interdependency between words, causes even more complication. This thesis presents novel document representations to overcome the two challenges. First, I employ label characteristics to estimate a compact document representation. B
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Velander, Alice, and Harrysson David Gumpert. "Do Judge a Book by its Cover! : Predicting the genre of book covers using supervised deep learning. Analyzing the model predictions using explanatory artificial intelligence methods and techniques." Thesis, Linköpings universitet, Datorseende, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-177691.

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In Storytel’s application on which a user can read and listen to digitalized literature, a user is displayed a list of books where the first thing the user encounters is the book title and cover. A book cover is therefore essential to attract a consumer’s attention. In this study, we take a data-driven approach to investigate the design principles for book covers through deep learning models and explainable AI. The first aim is to explore how well a Convolutional Neural Network (CNN) can interpret and classify a book cover image according to its genre in a multi-class classification task. The
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Lindkvist, Emilie. "Learning-by-modeling : Novel Computational Approaches for Exploring the Dynamics of Learning and Self-governance in Social-ecological Systems." Doctoral thesis, Stockholms universitet, Stockholm Resilience Centre, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-122395.

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As a consequence of global environmental change, sustainable management and governance of natural resources face critical challenges, such as dealing with non-linear dynamics, increased resource variability, and uncertainty. This thesis seeks to address some of these challenges by using simulation models. The first line of research focuses on the use of learning-by-doing (LBD) for managing a renewable resource, exemplified by a fish stock, and explores LBD in a theoretical model using artificial intelligence (Paper I and II). The second line of research focuses on the emergence of different fo
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Jezequel, Loïc. "Vers une détection d'anomalie unifiée avec une application à la détection de fraude." Electronic Thesis or Diss., CY Cergy Paris Université, 2023. http://www.theses.fr/2023CYUN1190.

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La détection d'observation s'écartant d'un cas de référence est cruciale dans de nombreuses applications. Cette problématique est présente dans la détection de fraudes, l'imagerie médicale, voire même la surveillance vidéo avec des données allant d'image aux sons. La détection d'anomalie profonde a été introduite dans cette optique, en modélisant la classe normale et en considérant tout ce qui est significativement différent comme étant anormal. Dans la mesure où la classe anormale n'est pas bien définie, une classification binaire classique manquerait de robustesse et de fiabilité sur des don
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Guerreiro, Lucas [UNESP]. "Aprendizado semi-supervisionado utilizando modelos de caminhada de partículas em grafos." Universidade Estadual Paulista (UNESP), 2017. http://hdl.handle.net/11449/151923.

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Oliveira, Clayton Silva. "Classificadores baseados em vetores de suporte gerados a partir de dados rotulados e não-rotulados." Universidade de São Paulo, 2006. http://www.teses.usp.br/teses/disponiveis/3/3152/tde-22072007-192518/.

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Treinamento semi-supervisionado é uma metodologia de aprendizado de máquina que conjuga características de treinamento supervisionado e não-supervisionado. Ela se baseia no uso de bases semi-rotuladas (bases contendo dados rotulados e não-rotulados) para o treinamento de classificadores. A adição de dados não-rotulados, mais baratos e geralmente disponíveis em maior quantidade do que os dados rotulados, pode aumentar o desempenho e/ou baratear o custo de treinamento desses classificadores (a partir da diminuição da quantidade de dados rotulados necessários). Esta dissertação analisa duas estra
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Bharti, Pratool. "Context-based Human Activity Recognition Using Multimodal Wearable Sensors." Scholar Commons, 2017. http://scholarcommons.usf.edu/etd/7000.

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In the past decade, Human Activity Recognition (HAR) has been an important part of the regular day to day life of many people. Activity recognition has wide applications in the field of health care, remote monitoring of elders, sports, biometric authentication, e-commerce and more. Each HAR application needs a unique approach to provide solutions driven by the context of the problem. In this dissertation, we are primarily discussing two application of HAR in different contexts. First, we design a novel approach for in-home, fine-grained activity rec
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Jones, Joshua K. "Empirically-based self-diagnosis and repair of domain knowledge." Diss., Georgia Institute of Technology, 2009. http://hdl.handle.net/1853/33931.

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In this work, I view incremental experiential learning in intelligent software agents as progressive agent self-adaptation. When an agent produces an incorrect behavior, then it may reflect on, and thus diagnose and repair, the reasoning and knowledge that produced the incorrect behavior. In particular, I focus on the self-diagnosis and self-repair of an agent's domain knowledge. The implementation of systems with the capability to self-diagnose and self-repair involves building both reasoning processes capable of such learning and knowledge representations capable of supporting those reaso
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IAPAOLO, FABIO. "De-Individuation of the Modern Subject in the Age of Artificial Intelligence. The Case of Self-Driving Cars and Algorithms for Decision Making." Doctoral thesis, Politecnico di Torino, 2021. http://hdl.handle.net/11583/2875755.

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Chandra, Nagasai. "Node Classification on Relational Graphs using Deep-RGCNs." DigitalCommons@CalPoly, 2021. https://digitalcommons.calpoly.edu/theses/2265.

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Knowledge Graphs are fascinating concepts in machine learning as they can hold usefully structured information in the form of entities and their relations. Despite the valuable applications of such graphs, most knowledge bases remain incomplete. This missing information harms downstream applications such as information retrieval and opens a window for research in statistical relational learning tasks such as node classification and link prediction. This work proposes a deep learning framework based on existing relational convolutional (R-GCN) layers to learn on highly multi-relational data cha
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Matsubara, Edson Takashi. "Relações entre ranking, análise ROC e calibração em aprendizado de máquina." Universidade de São Paulo, 2008. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-04032009-114050/.

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Aprendizado supervisionado tem sido principalmente utilizado para classificação. Neste trabalho são mostrados os benefícios do uso de rankings ao invés de classificação de exemplos isolados. Um rankeador é um algoritmo que ordena um conjunto de exemplos de tal modo que eles são apresentados do exemplo de maior para o exemplo de menor expectativa de ser positivo. Um ranking é o resultado dessa ordenação. Normalmente, um ranking é obtido pela ordenação do valor de confiança de classificação dado por um classificador. Este trabalho tem como objetivo procurar por novas abordagens para promover o u
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Oliverio, Vinicius. "Detecção de contradições em um sistema de aprendizado sem fim." Universidade Federal de São Carlos, 2012. https://repositorio.ufscar.br/handle/ufscar/505.

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Made available in DSpace on 2016-06-02T19:05:58Z (GMT). No. of bitstreams: 1 4650.pdf: 1170921 bytes, checksum: a9b0c215ae5f2804a8bbdd8bd2267ca8 (MD5) Previous issue date: 2012-06-29<br>Universidade Federal de Sao Carlos<br>NELL (Never Ending Language Learning) is a system that seeks to learn in an infinite way, extracting structured information from unstructured web pages using the semi-supervised learning paradigm as one of its basic principles. The Read the Web (RTW) project is the project where the NELL system is contained, actually it consists of 5 modules, all of them working indepen
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Gal, Jocelyn. "Application d’algorithmes de machine learning pour l’exploitation de données omiques en oncologie." Electronic Thesis or Diss., Université Côte d'Azur (ComUE), 2019. http://theses.univ-cotedazur.fr/2019AZUR6026.

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Le développement de l’informatique en médecine et en biologie a permis de générer un grand volume de données. La complexité et la quantité d’informations à intégrer lors d’une prise de décision médicale ont largement dépassé les capacités humaines. Ces informations comprennent des variables démographiques, cliniques ou radiologiques mais également des variables biologiques et en particulier omiques (génomique, protéomique, transcriptomique et métabolomique) caractérisées par un grand nombre de variables mesurées relativement au faible nombre de patients. Leur analyse représente un véritable dé
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Sörman, Paulsson Elsa. "Evaluation of In-Silico Labeling for Live Cell Imaging." Thesis, Umeå universitet, Institutionen för fysik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-180590.

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Today new drugs are tested on cell cultures in wells to minimize time, cost, andanimal testing. The cells are studied using microscopy in different ways and fluorescentprobes are used to study finer details than the light microscopy can observe.This is an invasive method, so instead of molecular analysis, imaging can be used.In this project, phase-contrast microscopy images of cells together with fluorescentmicroscopy images were used. We use Machine Learning to predict the fluorescentimages from the light microscopy images using a strategy called In-Silico Labeling.A Convolutional Neural Netw
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Krundel, Ludovic. "On microelectronic self-learning cognitive chip systems." Thesis, Loughborough University, 2016. https://dspace.lboro.ac.uk/2134/21804.

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After a brief review of machine learning techniques and applications, this Ph.D. thesis examines several approaches for implementing machine learning architectures and algorithms into hardware within our laboratory. From this interdisciplinary background support, we have motivations for novel approaches that we intend to follow as an objective of innovative hardware implementations of dynamically self-reconfigurable logic for enhanced self-adaptive, self-(re)organizing and eventually self-assembling machine learning systems, while developing this new particular area of research. And after revi
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Andersson, Melanie, Arvola Maja, and Sara Hedar. "Sketch Classification with Neural Networks : A Comparative Study of CNN and RNN on the Quick, Draw! data set." Thesis, Uppsala universitet, Institutionen för teknikvetenskaper, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-353504.

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The aim of the study is to apply and compare the performance of two different types of neural networks on the Quick, Draw! dataset and from this determine whether interpreting the sketches as sequences gives a higher accuracy than interpreting them as pixels. The two types of networks constructed were a recurrent neural network (RNN) and a convolutional neural network (CNN). The networks were optimised and the final architectures included five layers. The final evaluation accuracy achieved was 94.2% and 92.3% respectively, leading to the conclusion that the sequential interpretation of the Qui
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44

Di, Marco Lionel. "Récit d'ingénierie pédagogique en santé à l'usage de l'enseignant connecté Does the acceptance of hybrid learning affect learning approaches in France? Blended Learning for French Health Students: Does Acceptance of a Learning Management System Influence Students’ Self-Efficacy?" Thesis, Université Grenoble Alpes, 2020. http://www.theses.fr/2020GRALS024.

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Contexte. L’objectif général de ce travail de thèse était d’évaluer une méthode pédagogique hybride utilisant un environnement informatique pour l’apprentissage humain (EIAH) dans le cadre de la formation des professionnels de santé. Trois questions de recherche se sont ainsi succédé. L’acceptabilité de l’apprentissage hybride influe-t-elle sur les stratégies et approches d’apprentissage des étudiants ? L’acceptabilité d’un EIAH influe-t-elle sur le sentiment d’auto-efficacité des étudiants ? Quelles caractéristiques d’un cours dématérialisé fait varier l’attention des étudiants ?Matériels &am
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Albilani, Mohamad. "Neuro-symbolic deep reinforcement learning for safe urban driving using low-cost sensors." Electronic Thesis or Diss., Institut polytechnique de Paris, 2024. http://www.theses.fr/2024IPPAS008.

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La recherche effectuée dans cette thèse concerne le domaine de la conduite urbaine sûre, en utilisant des méthodes de fusion de capteurs et d'apprentissage par renforcement pour la perception et le contrôle des véhicules autonomes (VA). L'évolution généralisée des technologies d'apprentissage automatique ont principalement propulsé la prolifération des véhicules autonomes ces dernières années. Cependant, des progrès substantiels sont nécessaires avant d'atteindre une adoption généralisée par le grand public. Pour accomplir son automatisation, les véhicules autonomes nécessitent l'intégration d
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Sarwar, Fahad Ali. "Innovative self-optimizing control of building microgrids exploiting hydrogen multiple services potential." Electronic Thesis or Diss., Bordeaux, 2025. http://www.theses.fr/2025BORD0058.

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La croissance continue de la demande énergétique et l'impératif de décarbonation nécessitent l’intégration accrue des sources d’énergie renouvelables dans les micro-réseaux et les bâtiments. Toutefois, en raison de leur nature intermittente, l’intégration de ces sources renouvelables impose le recours à des systèmes de stockage d’énergie afin d’assurer la stabilité et l’autonomie des réseaux. Cette exigence est particulièrement importante dans le cadre de l’autoconsommation des bâtiments et des micro-réseaux à énergie nette zéro. Bien que les batteries conventionnelles soient largement utilisé
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Dsouza, Rodney Gracian. "Deep Learning Based Motion Forecasting for Autonomous Driving." The Ohio State University, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=osu1619139403696822.

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Giuliani, Luca. "Extending the Moving Targets Method for Injecting Constraints in Machine Learning." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/23885/.

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Informed Machine Learning is an umbrella term that comprises a set of methodologies in which domain knowledge is injected into a data-driven system in order to improve its level of accuracy, satisfy some external constraint, and in general serve the purposes of explainability and reliability. The said topid has been widely explored in the literature by means of many different techniques. Moving Targets is one such a technique particularly focused on constraint satisfaction: it is based on decomposition and bi-level optimization and proceeds by iteratively refining the target labels through a m
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Ansarnia, Masoomeh. "Development and Test of Computer Vision and Deep Learning Methods for Dynamic Management of Urban Lighting." Electronic Thesis or Diss., Université de Lorraine, 2023. http://www.theses.fr/2023LORR0272.

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Cette thèse de doctorat est réalisée dans le cadre d'un contrat de recherche entre la Société Eclatec, concepteur et fabricant français de luminaires urbains, et l'Institut Jean Lamour de Nancy. L'objectif concerne l'amélioration de l'éclairage nocturne des villes tout en réduisant la consommation électrique et la pollution lumineuse. Pour cela une caméra RGB est intégrée à la source lumineuse du lampadaire. Elle constitue le principal point de collecte d'informations. Ce choix a imposé l'utilisation d'une optique très grand angle dont l'axe est faiblement incliné par rapport à la verticale. L
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Rabe, Erik, and Zacharias Sundlöf. "Bidragande faktorer till attityder gentemot implementering av AI-styrda fordon." Thesis, Uppsala universitet, Institutionen för informatik och media, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-417547.

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Artificiell intelligens är en form av teknik som blir vanligare inom samhället. I takt med att tekniken utvecklas blir även diskussionen inom området mer utvecklad vilket resulterat i att eventuella problem och möjligheter blivit mer tillgänglig information. Det finns en avsaknad av tankar och förväntningar från privatpersoners synvinkel inom ämnet vilket kan ses som negativt då de förväntas vara en majoritet av användarbasen för tekniken. Eftersom denna typ av teknik förutspås ta över ett större ansvar av mänskliga uppgifter är det viktigt att klarlägga olika typer av tillvägagångssätt samt u
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