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Rozprawy doktorskie na temat „Multiple Sparse Bayesian Learning”

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1

Higson, Edward John. "Bayesian methods and machine learning in astrophysics." Thesis, University of Cambridge, 2019. https://www.repository.cam.ac.uk/handle/1810/289728.

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This thesis is concerned with methods for Bayesian inference and their applications in astrophysics. We principally discuss two related themes: advances in nested sampling (Chapters 3 to 5), and Bayesian sparse reconstruction of signals from noisy data (Chapters 6 and 7). Nested sampling is a popular method for Bayesian computation which is widely used in astrophysics. Following the introduction and background material in Chapters 1 and 2, Chapter 3 analyses the sampling errors in nested sampling parameter estimation and presents a method for estimating them numerically for a single nested sam

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Parisi, Simone [Verfasser], Jan [Akademischer Betreuer] Peters, and Joschka [Akademischer Betreuer] Boedeker. "Reinforcement Learning with Sparse and Multiple Rewards / Simone Parisi ; Jan Peters, Joschka Boedeker." Darmstadt : Universitäts- und Landesbibliothek Darmstadt, 2020. http://d-nb.info/1203301545/34.

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Tandon, Prateek. "Bayesian Aggregation of Evidence for Detection and Characterization of Patterns in Multiple Noisy Observations." Research Showcase @ CMU, 2015. http://repository.cmu.edu/dissertations/658.

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Effective use of Machine Learning to support extracting maximal information from limited sensor data is one of the important research challenges in robotic sensing. This thesis develops techniques for detecting and characterizing patterns in noisy sensor data. Our Bayesian Aggregation (BA) algorithmic framework can leverage data fusion from multiple low Signal-To-Noise Ratio (SNR) sensor observations to boost the capability to detect and characterize the properties of a signal generating source or process of interest. We illustrate our research with application to the nuclear threat detection

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Ticlavilca, Andres M. "Multivariate Bayesian Machine Learning Regression for Operation and Management of Multiple Reservoir, Irrigation Canal, and River Systems." DigitalCommons@USU, 2010. https://digitalcommons.usu.edu/etd/600.

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The principal objective of this dissertation is to develop Bayesian machine learning models for multiple reservoir, irrigation canal, and river system operation and management. These types of models are derived from the emerging area of machine learning theory; they are characterized by their ability to capture the underlying physics of the system simply by examination of the measured system inputs and outputs. They can be used to provide probabilistic predictions of system behavior using only historical data. The models were developed in the form of a multivariate relevance vector machine (MV

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Jin, Junyang. "Novel methods for biological network inference : an application to circadian Ca2+ signaling network." Thesis, University of Cambridge, 2018. https://www.repository.cam.ac.uk/handle/1810/285323.

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Biological processes involve complex biochemical interactions among a large number of species like cells, RNA, proteins and metabolites. Learning these interactions is essential to interfering artificially with biological processes in order to, for example, improve crop yield, develop new therapies, and predict new cell or organism behaviors to genetic or environmental perturbations. For a biological process, two pieces of information are of most interest. For a particular species, the first step is to learn which other species are regulating it. This reveals topology and causality. The second

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Yazdani, Akram. "Statistical Approaches in Genome-Wide Association Studies." Doctoral thesis, Università degli studi di Padova, 2014. http://hdl.handle.net/11577/3423743.

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Genome-wide association studies, GWAS, typically contain hundreds of thousands single nucleotide polymorphisms, SNPs, genotyped for few numbers of samples. The aim of these studies is to identify regions harboring SNPs or to predict the outcomes of interest. Since the number of predictors in the GWAS far exceeds the number of samples, it is impossible to analyze the data with classical statistical methods. In the current GWAS, the widely applied methods are based on single marker analysis that does assess association of each SNP with the complex traits independently. Because of the low pow

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Deshpande, Hrishikesh. "Dictionary learning for pattern classification in medical imaging." Thesis, Rennes 1, 2016. http://www.theses.fr/2016REN1S032/document.

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La plupart des signaux naturels peuvent être représentés par une combinaison linéaire de quelques atomes dans un dictionnaire. Ces représentations parcimonieuses et les méthodes d'apprentissage de dictionnaires (AD) ont suscité un vif intérêt au cours des dernières années. Bien que les méthodes d'AD classiques soient efficaces dans des applications telles que le débruitage d'images, plusieurs méthodes d'AD discriminatifs ont été proposées pour obtenir des dictionnaires mieux adaptés à la classification. Dans ce travail, nous avons montré que la taille des dictionnaires de chaque classe est un

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Chen, Cong. "High-Dimensional Generative Models for 3D Perception." Diss., Virginia Tech, 2021. http://hdl.handle.net/10919/103948.

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Modern robotics and automation systems require high-level reasoning capability in representing, identifying, and interpreting the three-dimensional data of the real world. Understanding the world's geometric structure by visual data is known as 3D perception. The necessity of analyzing irregular and complex 3D data has led to the development of high-dimensional frameworks for data learning. Here, we design several sparse learning-based approaches for high-dimensional data that effectively tackle multiple perception problems, including data filtering, data recovery, and data retrieval. The fram

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Subramanian, Harshavardhan. "Combining scientific computing and machine learning techniques to model longitudinal outcomes in clinical trials." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-176427.

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Scientific machine learning (SciML) is a new branch of AI research at the edge of scientific computing (Sci) and machine learning (ML). It deals with efficient amalgamation of data-driven algorithms along with scientific computing to discover the dynamics of the time-evolving process. The output of such algorithms is represented in the form of a governing equation(s) (e.g., ordinary differential equation(s), ODE(s)), which one can solve then for any time point and, thus, obtain a rigorous prediction. In this thesis, we present a methodology on how to incorporate the SciML approach in the cont

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Francisco, André Biasin Segalla. "Esparsidade estruturada em reconstrução de fontes de EEG." Universidade de São Paulo, 2018. http://www.teses.usp.br/teses/disponiveis/43/43134/tde-13052018-112615/.

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Neuroimagiologia funcional é uma área da neurociência que visa o desenvolvimento de diversas técnicas para mapear a atividade do sistema nervoso e esteve sob constante desenvolvimento durante as últimas décadas devido à sua grande importância para aplicações clínicas e pesquisa. Técnicas usualmente utilizadas, como imagem por ressonância magnética functional (fMRI) e tomografia por emissão de pósitrons (PET) têm ótima resolução espacial (~ mm), mas uma resolução temporal limitada (~ s), impondo um grande desafio para nossa compreensão a respeito da dinâmica de funções cognitivas mais elevadas,

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Zambonin, Giuliano. "Development of Machine Learning-based technologies for major appliances: soft sensing for drying technology applications." Doctoral thesis, Università degli studi di Padova, 2019. http://hdl.handle.net/11577/3425771.

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In this thesis, Machine Learning techniques for the improvements in the performance of household major appliances are described. In particular, the focus is on drying technologies and domestic dryers are the machines of interest selected as case studies. Statistical models called Soft Sensors have been developed to provide estimates of quantities that are costly/time-consuming to measure in our applications using data that were available for other purposes. The work has been developed as industrially driven research activity in collaborations with Electrolux Italia S.p.a. R&D department locat

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Umakanthan, Sabanadesan. "Human action recognition from video sequences." Thesis, Queensland University of Technology, 2016. https://eprints.qut.edu.au/93749/1/Sabanadesan_Umakanthan_Thesis.pdf.

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This PhD research has proposed new machine learning techniques to improve human action recognition based on local features. Several novel video representation and classification techniques have been proposed to increase the performance with lower computational complexity. The major contributions are the construction of new feature representation techniques, based on advanced machine learning techniques such as multiple instance dictionary learning, Latent Dirichlet Allocation (LDA) and Sparse coding. A Binary-tree based classification technique was also proposed to deal with large amounts of a

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Azevedo, Carlos Renato Belo 1984. "Anticipation in multiple criteria decision-making under uncertainty = Antecipação na tomada de decisão com múltiplos critérios sob incerteza." [s.n.], 2012. http://repositorio.unicamp.br/jspui/handle/REPOSIP/260775.

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Orientador: Fernando José Von Zuben<br>Tese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de Computação<br>Made available in DSpace on 2018-08-26T06:49:07Z (GMT). No. of bitstreams: 1 Azevedo_CarlosRenatoBelo_D.pdf: 3449858 bytes, checksum: 7a1811aa772f1ae996e8851c60627b7c (MD5) Previous issue date: 2012<br>Resumo: A presença de incerteza em resultados futuros pode levar a indecisões em processos de escolha, especialmente ao elicitar as importâncias relativas de múltiplos critérios de decisão e de desempenhos de curto vs. longo prazo. Algumas decisões, n

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14

Cherief-Abdellatif, Badr-Eddine. "Contributions to the theoretical study of variational inference and robustness." Electronic Thesis or Diss., Institut polytechnique de Paris, 2020. http://www.theses.fr/2020IPPAG001.

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Cette thèse de doctorat traite de l'inférence variationnelle et de la robustesse en statistique et en machine learning. Plus précisément, elle se concentre sur les propriétés statistiques des approximations variationnelles et sur la conception d'algorithmes efficaces pour les calculer de manière séquentielle, et étudie les estimateurs basés sur le Maximum Mean Discrepancy comme règles d'apprentissage qui sont robustes à la mauvaise spécification du modèle.Ces dernières années, l'inférence variationnelle a été largement étudiée du point de vue computationnel, cependant, la littérature n'a accor

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Wolley, Chirine. "Apprentissage supervisé à partir des multiples annotateurs incertains." Thesis, Aix-Marseille, 2014. http://www.theses.fr/2014AIXM4070/document.

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En apprentissage supervisé, obtenir les réels labels pour un ensemble de données peut être très fastidieux et long. Aujourd'hui, les récentes avancées d'Internet ont permis le développement de services d'annotations en ligne, faisant appel au crowdsourcing pour collecter facilement des labels. Néanmoins, le principal inconvénient de ces services réside dans le fait que les annotateurs peuvent avoir des niveaux d'expertise très hétérogènes. De telles données ne sont alors pas forcément fiables. Par conséquent, la gestion de l'incertitude des annotateurs est un élément clé pour l'apprentissage à

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Behúň, Kamil. "Příznaky z videa pro klasifikaci." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2013. http://www.nusl.cz/ntk/nusl-236367.

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This thesis compares hand-designed features with features learned by feature learning methods in video classification. The features learned by Principal Component Analysis whitening, Independent subspace analysis and Sparse Autoencoders were tested in a standard Bag of Visual Word classification paradigm replacing hand-designed features (e.g. SIFT, HOG, HOF). The classification performance was measured on Human Motion DataBase and YouTube Action Data Set. Learned features showed better performance than the hand-desined features. The combination of hand-designed features and learned features by

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Le, Folgoc Loïc. "Apprentissage statistique pour la personnalisation de modèles cardiaques à partir de données d’imagerie." Thesis, Nice, 2015. http://www.theses.fr/2015NICE4098/document.

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Cette thèse porte sur un problème de calibration d'un modèle électromécanique de cœur, personnalisé à partir de données d'imagerie médicale 3D+t ; et sur celui - en amont - de suivi du mouvement cardiaque. A cette fin, nous adoptons une méthodologie fondée sur l'apprentissage statistique. Pour la calibration du modèle mécanique, nous introduisons une méthode efficace mêlant apprentissage automatique et une description statistique originale du mouvement cardiaque utilisant la représentation des courants 3D+t. Notre approche repose sur la construction d'un modèle statistique réduit reliant l'esp

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Dang, Hong-Phuong. "Approches bayésiennes non paramétriques et apprentissage de dictionnaire pour les problèmes inverses en traitement d'image." Thesis, Ecole centrale de Lille, 2016. http://www.theses.fr/2016ECLI0019/document.

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L'apprentissage de dictionnaire pour la représentation parcimonieuse est bien connu dans le cadre de la résolution de problèmes inverses. Les méthodes d'optimisation et les approches paramétriques ont été particulièrement explorées. Ces méthodes rencontrent certaines limitations, notamment liées au choix de paramètres. En général, la taille de dictionnaire doit être fixée à l'avance et une connaissance des niveaux de bruit et éventuellement de parcimonie sont aussi nécessaires. Les contributions méthodologies de cette thèse concernent l'apprentissage conjoint du dictionnaire et de ces paramètr

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Gerchinovitz, Sébastien. "Prédiction de suites individuelles et cadre statistique classique : étude de quelques liens autour de la régression parcimonieuse et des techniques d'agrégation." Phd thesis, Université Paris Sud - Paris XI, 2011. http://tel.archives-ouvertes.fr/tel-00653550.

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Cette thèse s'inscrit dans le domaine de l'apprentissage statistique. Le cadre principal est celui de la prévision de suites déterministes arbitraires (ou suites individuelles), qui recouvre des problèmes d'apprentissage séquentiel où l'on ne peut ou ne veut pas faire d'hypothèses de stochasticité sur la suite des données à prévoir. Cela conduit à des méthodes très robustes. Dans ces travaux, on étudie quelques liens étroits entre la théorie de la prévision de suites individuelles et le cadre statistique classique, notamment le modèle de régression avec design aléatoire ou fixe, où les données

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Prasad, Ranjitha. "Sparse Bayesian Learning For Joint Channel Estimation Data Detection In OFDM Systems." Thesis, 2015. http://etd.iisc.ernet.in/2005/3997.

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Bayesian approaches for sparse signal recovery have enjoyed a long-standing history in signal processing and machine learning literature. Among the Bayesian techniques, the expectation maximization based Sparse Bayesian Learning(SBL) approach is an iterative procedure with global convergence guarantee to a local optimum, which uses a parameterized prior that encourages sparsity under an evidence maximization frame¬work. SBL has been successfully employed in a wide range of applications ranging from image processing to communications. In this thesis, we propose novel, efficient and low-complexi

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Shi, Minghui. "Bayesian Sparse Learning for High Dimensional Data." Diss., 2011. http://hdl.handle.net/10161/3869.

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<p>In this thesis, we develop some Bayesian sparse learning methods for high dimensional data analysis. There are two important topics that are related to the idea of sparse learning -- variable selection and factor analysis. We start with Bayesian variable selection problem in regression models. One challenge in Bayesian variable selection is to search the huge model space adequately, while identifying high posterior probability regions. In the past decades, the main focus has been on the use of Markov chain Monte Carlo (MCMC) algorithms for these purposes. In the first part of this thesis, i

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Huang, Din-Hwa, and 黃汀華. "Basis Adaptive Sparse Bayesian Learning : Algorithms and Applications." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/6n47p5.

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博士<br>國立交通大學<br>電信工程研究所<br>103<br>Sparse Bayesian learning (SBL) is a widely used compressive sensing (CS) method that finds the solution by Bayesian inference. In this approach, a basis function is specified to form the transform matrix. For a particular application, it may exist a proper basis, with known model function and unknown parameters, which can convert the signal to a sparse domain. In conventional SBL, the parameters of the basis are assumed to be known as priori. This assumption may not be valid in real-world applications, and the efficacy of conventional SBL approaches can be gre

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Huang, Wen-Han, and 黃玟翰. "Three-dimensional probabilistic site characterization by sparse Bayesian learning." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/6u62y3.

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碩士<br>國立臺灣大學<br>土木工程學研究所<br>107<br>This study investigated the modified cone tip resistance (qt) data from cone penetration tests (CPT). The feasibility and method of identifying the trend function were also discussed. The vertical spatial distribution is expressed as a depth-dependent trend function and a zero-mean spatial variation. Trend function can help us catch soil properties in space. Spatial variation can be estimated by standard deviation (σ) and scale of fluctuation (δ). In addition to the vertical scale of fluctuation, in 3D case, horizontal scale of fluctuation is also important.

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Parisi, Simone. "Reinforcement Learning with Sparse and Multiple Rewards." Phd thesis, 2020. https://tuprints.ulb.tu-darmstadt.de/11372/1/THESIS.PDF.

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Over the course of the last decade, the framework of reinforcement learning has developed into a promising tool for learning a large variety of task. The idea of reinforcement learning is, at its core, very simple yet effective. The learning agent is left to explore the world by performing actions based on its observations of the state of the world, and in turn receives a feedback, called reward, assessing the quality of its behavior. However, learning soon becomes challenging and even impractical as the complexity of the environment and of the task increase. In particular, learning without

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Huang, Han-Shen, and 黃漢申. "Learning from Sparse Data: An Approach to Parameter Learning in Bayesian Networks." Thesis, 2003. http://ndltd.ncl.edu.tw/handle/18831073237145141413.

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博士<br>國立臺灣大學<br>資訊工程學研究所<br>91<br>Many newly-emerging applications with small and incomplete (sparse for abbreviation) data sets present new challenges to machine learning. For example, we would like to have a model that can accurately predict the possibility of domestic terrorist incidents and attack terrorism in advance. Such incidents are rare, but always bring severe impact once they really happen. In addition, the relevant symptoms may be unknown, unobserved, and different case by case. Therefore, learning accu

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Kuen-FengLee and 李昆峯. "Construction of Document Model and Language Model Using Bayesian Sparse Learning." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/57056195766494950616.

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Tien-Yu, Hsieh, and 謝典佑. "Modeling Students' Learning Bugs and Skills Using Combining Multiple Bayesian Networks." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/06642102650546308286.

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碩士<br>國立臺中教育大學<br>數學教育學系<br>94<br>The goal of this paper is trying to develop fusion methods for combining multiple Bayesian networks and to obtain better classification results than single Bayesian networks. Six fusion methods, Maximum, Minimum, Average, Product, Majority Vote and Fusion Structure were proposed and evaluated based on educational test data. The results show that the proposed fusion methods, Structure Fusion, with dynamic cut-point selection can improve the classification accuracy.

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Kück, Hendrik. "Bayesian formulations of multiple instance learning with applications to general object recognition." Thesis, 2004. http://hdl.handle.net/2429/15680.

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We attack the problem of general object recognition by learning probabilistic, nonlinear object classifiers for a large number of object classes. The individual classifiers allow for detection and localization of objects belonging to a certain class in an image by classifying image regions into those that likely show such an object and those that do not. Instead of relying on expensive supervised training data, we propose an approach for learning such classifiers from annotated images. One major problem to overcome in this scenario is the ambiguity due to the unknown associations between annot

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Manandhar, Achut. "Hierarchical Bayesian Learning Approaches for Different Labeling Cases." Diss., 2015. http://hdl.handle.net/10161/11321.

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<p>The goal of a machine learning problem is to learn useful patterns from observations so that appropriate inference can be made from new observations as they become available. Based on whether labels are available for training data, a vast majority of the machine learning approaches can be broadly categorized into supervised or unsupervised learning approaches. In the context of supervised learning, when observations are available as labeled feature vectors, the learning process is a well-understood problem. However, for many applications, the standard supervised learning becomes complicated

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"Bayesian Framework for Sparse Vector Recovery and Parameter Bounds with Application to Compressive Sensing." Master's thesis, 2019. http://hdl.handle.net/2286/R.I.55639.

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abstract: Signal compressed using classical compression methods can be acquired using brute force (i.e. searching for non-zero entries in component-wise). However, sparse solutions require combinatorial searches of high computations. In this thesis, instead, two Bayesian approaches are considered to recover a sparse vector from underdetermined noisy measurements. The first is constructed using a Bernoulli-Gaussian (BG) prior distribution and is assumed to be the true generative model. The second is constructed using a Gamma-Normal (GN) prior distribution and is, therefore, a different (i.e. mi

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Yang, Chih-Wei, and 楊智為. "Modeling Student’s Learning Bugs and Skills Using Combining Multiple Modeling Student’s Learning Bugs and Skills Using Combining Bayesian Networks based on SVM." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/14681956954826606265.

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碩士<br>國立臺中教育大學<br>教育測驗統計研究所<br>95<br>The goal of this study was trying to combine the multiple Bayesian networks with classifier and to obtain better accuracy than single Bayesian network. The two classifiers, k-nearest-neighborhood, and support vector machine were used in this paper with two kinds of input, binary and posterior probability. The results showed that basing on the support vector machine to combine the multiple Bayesian networks with posterior probability can improve the classification accuracy.

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Rodrigues, Filipe Manuel Pereira Duarte. "Probabilistic models for learning from crowdsourced data." Doctoral thesis, 2016. http://hdl.handle.net/10316/29454.

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Tese de doutoramento em Programa de Doutoramento em Ciência da Informação e Tecnologia, apresentada ao Departamento de Engenharia Informática da Faculdade de Ciências e Tecnologia da Universidade de Coimbra<br>A presente tese propõe um conjunto de modelos probabilísticos para aprendizagem a partir de dados gerados pela multidão (crowd). Este tipo de dados tem vindo rapidamente a alterar a forma como muitos problemas de aprendizagem máquina são abordados em diferentes áreas do domínio científico, tais como o processamento de linguagem natural, a visão computacional e a música. Através da sabedo

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chang, chen jung, and 陳榮昌. "Adaptive learning system based on Bayesian network using fusion strategy for combining multiple student models -using compound shape for an example." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/50476915610782026926.

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碩士<br>亞洲大學<br>資訊工程學系碩士班<br>95<br>The main purpose of the research is to explore the educational assessment on the basis of Evidence-Centered Design（ECD） to build a convenient and effective diagnosis system. We use multiple Bayesian networks for modeling assessment data and identifying bugs and sub-skills in The “Compound Shape” of Mathematics in Grade 6. This research integrates the opinion of the experts, scholars and primary school teachers. Also, the multimedia computer is devised for Diagnostic Testing and computerizes adaptive remedial instruction with the system. Students can receive not

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Srinivas, Suraj. "Learning Compact Architectures for Deep Neural Networks." Thesis, 2017. http://etd.iisc.ernet.in/2005/3581.

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Deep neural networks with millions of parameters are at the heart of many state of the art computer vision models. However, recent works have shown that models with much smaller number of parameters can often perform just as well. A smaller model has the advantage of being faster to evaluate and easier to store - both of which are crucial for real-time and embedded applications. While prior work on compressing neural networks have looked at methods based on sparsity, quantization and factorization of neural network layers, we look at the alternate approach of pruning neurons. Training Neural N

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Divya, Padmanabhan. "New Methods for Learning from Heterogeneous and Strategic Agents." Thesis, 2017. http://etd.iisc.ernet.in/2005/3562.

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1 Introduction In this doctoral thesis, we address several representative problems that arise in the context of learning from multiple heterogeneous agents. These problems are relevant to many modern applications such as crowdsourcing and internet advertising. In scenarios such as crowdsourcing, there is a planner who is interested in learning a task and a set of noisy agents provide the training data for this learning task. Any learning algorithm making use of the data provided by these noisy agents must account for their noise levels. The noise levels of the agents are unknown to the planne

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(8086652), Guilherme Maia Rodrigues Gomes. "Hypothesis testing and community detection on networks with missingness and block structure." Thesis, 2019.

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Statistical analysis of networks has grown rapidly over the last few years with increasing number of applications. Graph-valued data carries additional information of dependencies which opens the possibility of modeling highly complex objects in vast number of fields such as biology (e.g. brain networks , fungi networks, genes co-expression), chemistry (e.g. molecules fingerprints), psychology (e.g. social networks) and many others (e.g. citation networks, word co-occurrences, financial systems, anomaly detection). While the inclusion of graph structure in the analysis can further help infere

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Ashofteh, Afshin. "Data Science for Finance: Targeted Learning from (Big) Data to Economic Stability and Financial Risk Management." Doctoral thesis, 2022. http://hdl.handle.net/10362/135620.

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A thesis submitted in partial fulfillment of the requirements for the degree of Doctor in Information Management, specialization in Statistics and Econometrics<br>The modelling, measurement, and management of systemic financial stability remains a critical issue in most countries. Policymakers, regulators, and managers depend on complex models for financial stability and risk management. The models are compelled to be robust, realistic, and consistent with all relevant available data. This requires great data disclosure, which is deemed to have the highest quality standards. However, stressed

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