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Relevant bibliographies by topics / Non-parametric learning / Dissertations / Theses

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Dissertations / Theses on the topic 'Non-parametric learning'

Author: Grafiati

Published: 25 May 2024

Last updated: 31 July 2025

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1

Zewdie, Dawit (Dawit Habtamu). "Representation discovery in non-parametric reinforcement learning." Thesis, Massachusetts Institute of Technology, 2014. http://hdl.handle.net/1721.1/91883.

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Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2014.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 71-73).<br>Recent years have seen a surge of interest in non-parametric reinforcement learning. There are now practical non-parametric algorithms that use kernel regression to approximate value functions. The correctness guarantees of kernel regression require that the underlying value function be smooth. Most problems of interest do not satisfy this requirement in their native space, but

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Campanholo, Guizilini Vitor. "Non-Parametric Learning for Monocular Visual Odometry." Thesis, The University of Sydney, 2013. http://hdl.handle.net/2123/9903.

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This thesis addresses the problem of incremental localization from visual information, a scenario commonly known as visual odometry. Current visual odometry algorithms are heavily dependent on camera calibration, using a pre-established geometric model to provide the transformation between input (optical flow estimates) and output (vehicle motion estimates) information. A novel approach to visual odometry is proposed in this thesis where the need for camera calibration, or even for a geometric model, is circumvented by the use of machine learning principles and techniques. A non-parametric Bay

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Bratières, Sébastien. "Non-parametric Bayesian models for structured output prediction." Thesis, University of Cambridge, 2018. https://www.repository.cam.ac.uk/handle/1810/274973.

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Structured output prediction is a machine learning tasks in which an input object is not just assigned a single class, as in classification, but multiple, interdependent labels. This means that the presence or value of a given label affects the other labels, for instance in text labelling problems, where output labels are applied to each word, and their interdependencies must be modelled. Non-parametric Bayesian (NPB) techniques are probabilistic modelling techniques which have the interesting property of allowing model capacity to grow, in a controllable way, with data complexity, while maint

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Prando, Giulia. "Non-Parametric Bayesian Methods for Linear System Identification." Doctoral thesis, Università degli studi di Padova, 2017. http://hdl.handle.net/11577/3426195.

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Recent contributions have tackled the linear system identification problem by means of non-parametric Bayesian methods, which are built on largely adopted machine learning techniques, such as Gaussian Process regression and kernel-based regularized regression. Following the Bayesian paradigm, these procedures treat the impulse response of the system to be estimated as the realization of a Gaussian process. Typically, a Gaussian prior accounting for stability and smoothness of the impulse response is postulated, as a function of some parameters (called hyper-parameters in the Bayesian framework

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Angola, Enrique. "Novelty Detection Of Machinery Using A Non-Parametric Machine Learning Approach." ScholarWorks @ UVM, 2018. https://scholarworks.uvm.edu/graddis/923.

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A novelty detection algorithm inspired by human audio pattern recognition is conceptualized and experimentally tested. This anomaly detection technique can be used to monitor the health of a machine or could also be coupled with a current state of the art system to enhance its fault detection capabilities. Time-domain data obtained from a microphone is processed by applying a short-time FFT, which returns time-frequency patterns. Such patterns are fed to a machine learning algorithm, which is designed to detect novel signals and identify windows in the frequency domain where such novelties occ

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Bartcus, Marius. "Bayesian non-parametric parsimonious mixtures for model-based clustering." Thesis, Toulon, 2015. http://www.theses.fr/2015TOUL0010/document.

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Cette thèse porte sur l’apprentissage statistique et l’analyse de données multi-dimensionnelles. Elle se focalise particulièrement sur l’apprentissage non supervisé de modèles génératifs pour la classiﬁcation automatique. Nous étudions les modèles de mélanges Gaussians, aussi bien dans le contexte d’estimation par maximum de vraisemblance via l’algorithme EM, que dans le contexte Bayésien d’estimation par Maximum A Posteriori via des techniques d’échantillonnage par Monte Carlo. Nous considérons principalement les modèles de mélange parcimonieux qui reposent sur une décomposition spectrale de

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Mahler, Nicolas. "Machine learning methods for discrete multi-scale fows : application to finance." Phd thesis, École normale supérieure de Cachan - ENS Cachan, 2012. http://tel.archives-ouvertes.fr/tel-00749717.

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This research work studies the problem of identifying and predicting the trends of a single financial target variable in a multivariate setting. The machine learning point of view on this problem is presented in chapter I. The efficient market hypothesis, which stands in contradiction with the objective of trend prediction, is first recalled. The different schools of thought in market analysis, which disagree to some extent with the efficient market hypothesis, are reviewed as well. The tenets of the fundamental analysis, the technical analysis and the quantitative analysis are made explicit.

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GONÇALVES, JÚNIOR Paulo Mauricio. "Multivariate non-parametric statistical tests to reuse classifiers in recurring concept drifting environments." Universidade Federal de Pernambuco, 2013. https://repositorio.ufpe.br/handle/123456789/12226.

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Data streams are a recent processing model where data arrive continuously, in large quantities, at high speeds, so that they must be processed on-line. Besides that, several private and public institutions store large amounts of data that also must be processed. Traditional batch classi ers are not well suited to handle huge amounts of data for basically two reasons. First, they usually read the available data several times until convergence, which is impractical in this scenario. Second, they imply that the context represented by data is stable in time, which may not be true. In fact, t

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Gonçalves, Júnior Paulo Mauricio. "Multivariate non-parametric statistical tests to reuse classifiers in recurring concept drifting environments." Universidade Federal de Pernambuco, 2013. https://repositorio.ufpe.br/handle/123456789/12288.

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Data streams are a recent processing model where data arrive continuously, in large quantities, at high speeds, so that they must be processed on-line. Besides that, several private and public institutions store large amounts of data that also must be processed. Traditional batch classi ers are not well suited to handle huge amounts of data for basically two reasons. First, they usually read the available data several times until convergence, which is impractical in this scenario. Second, they imply that the context represented by data is stable in time, which may not be true. In fact, t

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10

Wei, Wei. "Probabilistic Models of Topics and Social Events." Research Showcase @ CMU, 2016. http://repository.cmu.edu/dissertations/941.

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Structured probabilistic inference has shown to be useful in modeling complex latent structures of data. One successful way in which this technique has been applied is in the discovery of latent topical structures of text data, which is usually referred to as topic modeling. With the recent popularity of mobile devices and social networking, we can now easily acquire text data attached to meta information, such as geo-spatial coordinates and time stamps. This metadata can provide rich and accurate information that is helpful in answering many research questions related to spatial and temporal

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Eamrurksiri, Araya. "Applying Machine Learning to LTE/5G Performance Trend Analysis." Thesis, Linköpings universitet, Statistik och maskininlärning, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-139126.

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The core idea of this thesis is to reduce the workload of manual inspection when the performance analysis of an updated software is required. The Central Process- ing Unit (CPU) utilization, which is one of the essential factors for evaluating the performance, is analyzed. The purpose of this work is to apply machine learning techniques that are suitable for detecting the state of the CPU utilization and any changes in the test environment that affects the CPU utilization. The detection re- lies on a Markov switching model to identify structural changes, which are assumed to follow an unobserv

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Landoni, E. "A COMPREHENSIVE PIPELINE FOR CLASS COMPARISON AND CLASS PREDICTION IN CANCER RESEARCH." Doctoral thesis, Università degli Studi di Milano, 2015. http://hdl.handle.net/2434/344575.

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Personalized medicine is an emerging field that promises to bring radical changes in healthcare and may be defined as “a medical model using molecular profiling technologies for tailoring the right therapeutic strategy for the right person at the right time, and determine the predisposition to disease at the population level and to deliver timely and stratified prevention”. The sequencing of the human genome together with the development and implementation of new high throughput technologies has provided access to large ‘omics’ (e.g. genomics, proteomics) data, bringing a better understanding

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Aghazadeh, Omid. "Data Driven Visual Recognition." Doctoral thesis, KTH, Datorseende och robotik, CVAP, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-145865.

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This thesis is mostly about supervised visual recognition problems. Based on a general definition of categories, the contents are divided into two parts: one which models categories and one which is not category based. We are interested in data driven solutions for both kinds of problems. In the category-free part, we study novelty detection in temporal and spatial domains as a category-free recognition problem. Using data driven models, we demonstrate that based on a few reference exemplars, our methods are able to detect novelties in ego-motions of people, and changes in the static environme

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De, la Concha Duarte Alejandro David. "Graph-based machine learning for detection tasks on complex systems." Electronic Thesis or Diss., université Paris-Saclay, 2025. http://www.theses.fr/2025UPASM009.

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Les innovations technologiques récentes ont augmenté notre capacité à surveiller de nombreux aspects de la vie quotidienne ou des phénomènes naturels en collectant et en analysant des données en temps réel provenant de sources multiples. Par exemple, les réseaux de transport surveillent l'affluence des passagers et les retards à chaque station pour améliorer les trajets, tandis que les réseaux modernes de surveillance des risques géologiques déclenchent des alertes précoces pour des événements sismiques.Cette thèse se concentre sur la détection des changements dans les distributions de données

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van, der Wilk Mark. "Sparse Gaussian process approximations and applications." Thesis, University of Cambridge, 2019. https://www.repository.cam.ac.uk/handle/1810/288347.

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Many tasks in machine learning require learning some kind of input-output relation (function), for example, recognising handwritten digits (from image to number) or learning the motion behaviour of a dynamical system like a pendulum (from positions and velocities now to future positions and velocities). We consider this problem using the Bayesian framework, where we use probability distributions to represent the state of uncertainty that a learning agent is in. In particular, we will investigate methods which use Gaussian processes to represent distributions over functions. Gaussian process mo

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16

Lasserre, Marvin. "Apprentissages dans les réseaux bayésiens à base de copules non-paramétriques." Electronic Thesis or Diss., Sorbonne université, 2022. http://www.theses.fr/2022SORUS029.

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La modélisation de distributions continues multivariées est une tâche d'un intérêt central en statistiques et en apprentissage automatique avec de nombreuses applications en sciences et en ingénierie. Cependant, les distributions de grandes dimensions sont difficiles à manipuler et peuvent conduire à des calculs coûteux en temps et en ressources. Les réseaux bayésiens de copules (CBNs) tirent parti à la fois des réseaux bayésiens (BNs) et de la théorie des copules pour représenter de manière compacte de telles distributions multivariées. Les réseaux bayésiens s'appuient sur les indépendances c

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Shandilya, Sharad. "ASSESSMENT AND PREDICTION OF CARDIOVASCULAR STATUS DURING CARDIAC ARREST THROUGH MACHINE LEARNING AND DYNAMICAL TIME-SERIES ANALYSIS." VCU Scholars Compass, 2013. http://scholarscompass.vcu.edu/etd/3198.

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In this work, new methods of feature extraction, feature selection, stochastic data characterization/modeling, variance reduction and measures for parametric discrimination are proposed. These methods have implications for data mining, machine learning, and information theory. A novel decision-support system is developed in order to guide intervention during cardiac arrest. The models are built upon knowledge extracted with signal-processing, non-linear dynamic and machine-learning methods. The proposed ECG characterization, combined with information extracted from PetCO2 signals, shows viabi

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Hall, Otto. "Inference of buffer queue times in data processing systems using Gaussian Processes : An introduction to latency prediction for dynamic software optimization in high-end trading systems." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-214791.

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This study investigates whether Gaussian Process Regression can be applied to evaluate buffer queue times in large scale data processing systems. It is additionally considered whether high-frequency data stream rates can be generalized into a small subset of the sample space. With the aim of providing basis for dynamic software optimization, a promising foundation for continued research is introduced. The study is intended to contribute to Direct Market Access financial trading systems which processes immense amounts of market data daily. Due to certain limitations, we shoulder a naïve approac

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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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20

Knefati, Muhammad Anas. "Estimation non-paramétrique du quantile conditionnel et apprentissage semi-paramétrique : applications en assurance et actuariat." Thesis, Poitiers, 2015. http://www.theses.fr/2015POIT2280/document.

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La thèse se compose de deux parties : une partie consacrée à l'estimation des quantiles conditionnels et une autre à l'apprentissage supervisé. La partie "Estimation des quantiles conditionnels" est organisée en 3 chapitres : Le chapitre 1 est consacré à une introduction sur la régression linéaire locale, présentant les méthodes les plus utilisées, pour estimer le paramètre de lissage. Le chapitre 2 traite des méthodes existantes d’estimation nonparamétriques du quantile conditionnel ; Ces méthodes sont comparées, au moyen d’expériences numériques sur des données simulées et des données réelle

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Faury, Louis. "Variance-sensitive confidence intervals for parametric and offline bandits." Electronic Thesis or Diss., Institut polytechnique de Paris, 2021. http://www.theses.fr/2021IPPAT046.

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Cette thèse présente des contributions récentes au problème d’optimisation sous feedback bandit, au travers de la construction d’intervalles de confiance sensibles à la variance. Nous traitons deux aspects distincts du problème: (1) la minimisation du regret pour les bandits à modèle linéaire généralisé (GLBs), une large classe de bandits paramétriques non-linéaires et (2) le problème d’optimisation de politique hors ligne sous signal bandit. Concernant (1) nous étudions les effets de la non-linéarité dans les GLBs et remettons en question la compréhension actuelle selon laquelle des hauts niv

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SARCIA', SALVATORE ALESSANDRO. "An Approach to improving parametric estimation models in the case of violation of assumptions based upon risk analysis." Doctoral thesis, Università degli Studi di Roma "Tor Vergata", 2009. http://hdl.handle.net/2108/1048.

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In this work, we show the mathematical reasons why parametric models fall short of providing correct estimates and define an approach that overcomes the causes of these shortfalls. The approach aims at improving parametric estimation models when any regression model assumption is violated for the data being analyzed. Violations can be that, the errors are x-correlated, the model is not linear, the sample is heteroscedastic, or the error probability distribution is not Gaussian. If data violates the regression assumptions and we do not deal with the consequences of these violations, we cannot i

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23

Wang, Chunping. "Non-parametric Bayesian Learning with Incomplete Data." Diss., 2010. http://hdl.handle.net/10161/3075.

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<p>In most machine learning approaches, it is usually assumed that data are complete. When data are partially missing due to various reasons, for example, the failure of a subset of sensors, image corruption or inadequate medical measurements, many learning methods designed for complete data cannot be directly applied. In this dissertation we treat two kinds of problems with incomplete data using non-parametric Bayesian approaches: classification with incomplete features and analysis of low-rank matrices with missing entries.</p><p>Incomplete data in classification problems are handled by assu

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Castro, Rui M. "Active learning and adaptive sampling for non-parametric inference." Thesis, 2008. http://hdl.handle.net/1911/22265.

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This thesis presents a general discussion of active learning and adaptive sampling. In many practical scenarios it is possible to use information gleaned from previous observations to focus the sampling process, in the spirit of the "twenty-questions" game. As more samples are collected one can learn how to improve the sampling process by deciding where to sample next, for example. These sampling feedback techniques are generically known as active learning or adaptive sampling. Although appealing, analysis of such methodologies is difficult, since there are strong dependencies between the obs

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Amaro, Miguel Mendes. "Credit scoring: comparison of non‐parametric techniques against logistic regression." Master's thesis, 2020. http://hdl.handle.net/10362/99692.

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Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business Intelligence<br>Over the past decades, financial institutions have been giving increased importance to credit risk management as a critical tool to control their profitability. More than ever, it became crucial for these institutions to be able to well discriminate between good and bad clients for only accepting the credit applications that are not likely to default. To calculate the probability of default of a particular client, m

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"Computational Challenges in Non-parametric Prediction of Bradycardia in Preterm Infants." Master's thesis, 2020. http://hdl.handle.net/2286/R.I.63054.

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abstract: Infants born before 37 weeks of pregnancy are considered to be preterm. Typically, preterm infants have to be strictly monitored since they are highly susceptible to health problems like hypoxemia (low blood oxygen level), apnea, respiratory issues, cardiac problems, neurological problems as well as an increased chance of long-term health issues such as cerebral palsy, asthma and sudden infant death syndrome. One of the leading health complications in preterm infants is bradycardia - which is defined as the slower than expected heart rate, generally beating lower than 60 beats per mi

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(11197908), Yicheng Cheng. "Machine Learning in the Open World." Thesis, 2021.

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<div>By Machine Learning in the Open World, we are trying to build models that can be used in a more realistic setting where there could always be something "unknown" happening. Beyond the traditional machine learning tasks such as classification and segmentation where all classes are predefined, we are dealing with the challenges from newly emerged classes, irrelevant classes, outliers, and class imbalance.</div><div>At the beginning, we focus on the Non-Exhaustive Learning (NEL) problem from a statistical aspect. By NEL, we assume that our training classes are non-exhaustive, where the testi

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Pazis, Jason. "PAC-optimal, Non-parametric Algorithms and Bounds for Exploration in Concurrent MDPs with Delayed Updates." Diss., 2015. http://hdl.handle.net/10161/11334.

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<p>As the reinforcement learning community has shifted its focus from heuristic methods to methods that have performance guarantees, PAC-optimal exploration algorithms have received significant attention. Unfortunately, the majority of current PAC-optimal exploration algorithms are inapplicable in realistic scenarios: 1) They scale poorly to domains of realistic size. 2) They are only applicable to discrete state-action spaces. 3) They assume that experience comes from a single, continuous trajectory. 4) They assume that value function updates are instantaneous. The goal of this work is to bri

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"Graph-based Estimation of Information Divergence Functions." Doctoral diss., 2017. http://hdl.handle.net/2286/R.I.38649.

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abstract: Information divergence functions, such as the Kullback-Leibler divergence or the Hellinger distance, play a critical role in statistical signal processing and information theory; however estimating them can be challenge. Most often, parametric assumptions are made about the two distributions to estimate the divergence of interest. In cases where no parametric model fits the data, non-parametric density estimation is used. In statistical signal processing applications, Gaussianity is usually assumed since closed-form expressions for common divergence measures have been derived for thi

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