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Dissertations / Theses on the topic 'Kernel regression'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Dharmasena, Tibbotuwa Deniye Kankanamge Lasitha Sandamali, and Sandamali dharmasena@rmit edu au. "Sequential Procedures for Nonparametric Kernel Regression." RMIT University. Mathematical and Geospatial Sciences, 2008. http://adt.lib.rmit.edu.au/adt/public/adt-VIT20090119.134815.

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In a nonparametric setting, the functional form of the relationship between the response variable and the associated predictor variables is unspecified; however it is assumed to be a smooth function. The main aim of nonparametric regression is to highlight an important structure in data without any assumptions about the shape of an underlying regression function. In regression, the random and fixed design models should be distinguished. Among the variety of nonparametric regression estimators currently in use, kernel type estimators are most popular. Kernel type estimators provide a flexible c

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2

Brault, Romain. "Large-scale operator-valued kernel regression." Thesis, Université Paris-Saclay (ComUE), 2017. http://www.theses.fr/2017SACLE024/document.

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De nombreuses problématiques d'apprentissage artificiel peuvent être modélisées grâce à des fonctions à valeur vectorielles. Les noyaux à valeurs opérateurs et leur espace de Hilbert à noyaux reproduisant à valeurs vectorielles associés donnent un cadre théorique et pratique pour apprendre de telles fonctions, étendant la littérature existante des noyaux scalaires. Cependant, lorsque les données sont nombreuses, ces méthodes sont peu utilisables, ne passant pas à l'échelle, car elle nécessite une quantité de mémoire évoluant quadratiquement et un temps de calcul évoluant cubiquement vis à vis

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3

Zheng, Qi. "Local adaptive smoothing in kernel regression estimation." Connect to this title online, 2009.

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4

Hibraj, Feliks <1995&gt. "Efficient tensor kernel methods for sparse regression." Master's Degree Thesis, Università Ca' Foscari Venezia, 2020. http://hdl.handle.net/10579/16921.

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Recently, classical kernel methods have been extended by the introduction of suitable tensor kernels so to promote sparsity in the solution of the underlying regression problem. Indeed, they solve an lp-norm regularization problem, with p=m/(m-1) and m even integer, which happens to be close to a lasso problem. However, a major drawback of the method is that storing tensors requires a considerable amount of memory, ultimately limiting its applicability. In this work we address this problem by proposing two advances. First, we directly reduce the memory requirement, by introducing a new and mo

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5

Ren, Haobo. "Functional inverse regression and reproducing kernel Hilbert space." Diss., Texas A&M University, 2005. http://hdl.handle.net/1969.1/4203.

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The basic philosophy of Functional Data Analysis (FDA) is to think of the observed data functions as elements of a possibly infinite-dimensional function space. Most of the current research topics on FDA focus on advancing theoretical tools and extending existing multivariate techniques to accommodate the infinite-dimensional nature of data. This dissertation reports contributions on both fronts, where a unifying inverse regression theory for both the multivariate setting (Li 1991) and functional data from a Reproducing Kernel Hilbert Space (RKHS) prospective is developed. We proposed a functi

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6

Farooq, Muhammad Verfasser], and Ingo [Akademischer Betreuer] [Steinwart. "Kernel-based expectile regression / Muhammad Farooq ; Betreuer: Ingo Steinwart." Stuttgart : Universitätsbibliothek der Universität Stuttgart, 2017. http://d-nb.info/1148426337/34.

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Farooq, Muhammad [Verfasser], and Ingo [Akademischer Betreuer] Steinwart. "Kernel-based expectile regression / Muhammad Farooq ; Betreuer: Ingo Steinwart." Stuttgart : Universitätsbibliothek der Universität Stuttgart, 2017. http://d-nb.info/1148426337/34.

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8

Natarajan, Balasubramaniam. "Asymptotic properties of Non-parametric Regression with Beta Kernels." Diss., Kansas State University, 2017. http://hdl.handle.net/2097/38554.

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Doctor of Philosophy<br>Department of Statistics<br>Weixing Song<br>Kernel based non-parametric regression is a popular statistical tool to identify the relationship between response and predictor variables when standard parametric regression models are not appropriate. The efficacy of kernel based methods depend both on the kernel choice and the smoothing parameter. With insufficient smoothing, the resulting regression estimate is too rough and with excessive smoothing, important features of the underlying relationship is lost. While the choice of the kernel has been shown to have less of an

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9

DiPaolo, Conner. "Randomized Algorithms for Preconditioner Selection with Applications to Kernel Regression." Scholarship @ Claremont, 2019. https://scholarship.claremont.edu/hmc_theses/230.

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The task of choosing a preconditioner M to use when solving a linear system Ax=b with iterative methods is often tedious and most methods remain ad-hoc. This thesis presents a randomized algorithm to make this chore less painful through use of randomized algorithms for estimating traces. In particular, we show that the preconditioner stability || I - M-1A ||F, known to forecast preconditioner quality, can be computed in the time it takes to run a constant number of iterations of conjugate gradients through use of sketching methods. This is in spite of folklore which suggests the quantity is im

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10

Chan, Nigel Hiu Ngai. "Uniform convergence on cointegrating regression." Thesis, The University of Sydney, 2013. http://hdl.handle.net/2123/9807.

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Nonlinear cointegration model has been a popular tool for applied econometric modelling. There are numerous real life time series examples that demonstrate nonlinear response to another nonstationary time series in the field of macro-economics. The nonparametric estimation methods for the nonlinear linked function have been studied extensively in the literature. Most of the existing studies concentrate on establishing point-wise convergence, and there is little research on uniform convergence. The uniform convergence of nonparametric estimator of the nonlinear function is an important theoreti

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11

You, Di. "Model Selection in Kernel Methods." The Ohio State University, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=osu1322581224.

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12

Signorini, David F. "Practical aspects of kernel smoothing for binary regression and density estimation." Thesis, n.p, 1998. http://oro.open.ac.uk/19923/.

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13

Nicolle, Jérémie. "Reading Faces. Using Hard Multi-Task Metric Learning for Kernel Regression." Thesis, Paris 6, 2016. http://www.theses.fr/2016PA066043/document.

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Recueillir et labelliser un ensemble important et pertinent de données pour apprendre des systèmes de prédiction d'informations à partir de visages est à la fois difficile et long. Par conséquent, les données disponibles sont souvent de taille limitée comparée à la difficultés des tâches. Cela rend le problème du sur-apprentissage particulièrement important dans de nombreuses applications d'apprentissage statistique liées au visage. Dans cette thèse, nous proposons une nouvelle méthode de régression de labels multi-dimensionnels, nommée Hard Multi-Task Metric Learning for Kernel Regression (H-

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14

Rosipal, Roman. "Kernel-based regression and objective nonlinear measures to assess brain functioning." Thesis, University of the West of Scotland, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.398329.

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15

Xu, Haiyong. "Directional kernel regression and point process modeling in wildfire hazard assessment." Diss., Restricted to subscribing institutions, 2008. http://proquest.umi.com/pqdweb?did=1581420461&sid=1&Fmt=2&clientId=1564&RQT=309&VName=PQD.

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16

Wang, Sejong. "Three nonparametric specification tests for parametric regression models : the kernel estimation approach." Connect to resource, 1994. http://rave.ohiolink.edu/etdc/view.cgi?acc%5Fnum=osu1261492759.

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17

Petersson, David, and Emil Backman. "Change Point Detection and Kernel Ridge Regression for Trend Analysis on Financial Data." Thesis, KTH, Skolan för teknikvetenskap (SCI), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-230729.

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The investing market can be a cold ruthless place for the layman. In order to get the chance of making money in this business one must place countless hours on research, with many different parameters to handle in order to reach success. To reduce the risk, one must look to many different companies operating in multiple fields and industries. In other words, it can be a hard task to manage this feat. With modern technology, there is now lots of potential to handle this tedious analysis autonomously using machine learning and clever algorithms. With this approach, the amount of analyzes is only

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Kapat, Prasenjit. "Role of Majorization in Learning the Kernel within a Gaussian Process Regression Framework." The Ohio State University, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=osu1316521301.

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19

Maity, Arnab. "Efficient inference in general semiparametric regression models." [College Station, Tex. : Texas A&M University, 2008. http://hdl.handle.net/1969.1/ETD-TAMU-3075.

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Minnier, Jessica. "Inference and Prediction for High Dimensional Data via Penalized Regression and Kernel Machine Methods." Thesis, Harvard University, 2012. http://dissertations.umi.com/gsas.harvard:10327.

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Analysis of high dimensional data often seeks to identify a subset of important features and assess their effects on the outcome. Furthermore, the ultimate goal is often to build a prediction model with these features that accurately assesses risk for future subjects. Such statistical challenges arise in the study of genetic associations with health outcomes. However, accurate inference and prediction with genetic information remains challenging, in part due to the complexity in the genetic architecture of human health and disease. A valuable approach for improving prediction models with a lar

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21

Dougherty, Andrew W. "Intelligent Design of Metal Oxide Gas Sensor Arrays Using Reciprocal Kernel Support Vector Regression." The Ohio State University, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=osu1285045610.

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22

Samarakoon, Nishantha Anura. "Conditional variance function checking in heteroscedastic regression models." Diss., Kansas State University, 2011. http://hdl.handle.net/2097/10744.

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Doctor of Philosophy<br>Department of Statistics<br>Weixing Song<br>The regression model has been given a considerable amount of attention and played a significant role in data analysis. The usual assumption in regression analysis is that the variances of the error terms are constant across the data. Occasionally, this assumption of homoscedasticity on the variance is violated; and the data generated from real world applications exhibit heteroscedasticity. The practical importance of detecting heteroscedasticity in regression analysis is widely recognized in many applications because effi

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Wågberg, Johan, and Viklund Emanuel Walldén. "Continuous Occupancy Mapping Using Gaussian Processes." Thesis, Linköpings universitet, Reglerteknik, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-81464.

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The topic of this thesis is occupancy mapping for mobile robots, with an emphasis on a novel method for continuous occupancy mapping using Gaussian processes. In the new method, spatial correlation is accounted for in a natural way, and an a priori discretization of the area to be mapped is not necessary as within most other common methods. The main contribution of this thesis is the construction of a Gaussian process library for C++, and the use of this library to implement the continuous occupancy mapping algorithm. The continuous occupancy mapping is evaluated using both simulated and real

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24

Bouche, Dimitri. "Function-valued regression with kernels : Improving speed, flexibility and robustness." Electronic Thesis or Diss., Institut polytechnique de Paris, 2023. http://www.theses.fr/2023IPPAT001.

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L'augmentation du nombre et de la sophistication des appareils collectant des données permet de suivre l'évolution d'une multitude de phénomènes à des résolutions très fines. Cela étend le champ des applications possibles de l'apprentissage statistique. Un tel volume peut néanmoins devenir difficile à exploiter. Cependant quand leur nombre augmente, les données peuvent devenir redondantes. On peut alors chercher une représentation exploitant des propriétés du processus génératif. Dans cette thèse, nous nous concentrons sur la représentation fonctionnelle. Bien sûr, les données sont toujours de

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25

Sivaramakrishnan, Jayaram. "Unsupervised probabilistic and kernel regression methods for anomaly detection and parameter margin prediction of industrial design." Thesis, Sivaramakrishnan, Jayaram (2021) Unsupervised probabilistic and kernel regression methods for anomaly detection and parameter margin prediction of industrial design. PhD thesis, Murdoch University, 2021. https://researchrepository.murdoch.edu.au/id/eprint/62536/.

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One of the significant challenges facing industrial plant design is ensuring the integrity of massive design datasets generated during the project execution. This work is motivated by personal experience of data integrity issues during projects caused by insufficient automation affecting the quality of deliverables. Therefore, this project sought automated solutions for detecting anomalies in industrial design data in the form of outliers. Several novel methods are proposed, based on the Hidden Markov Model (HMM) and a modified General Regression Neural Network called the Margin-Based GRNN (MB

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26

Sanda, Rene. "Análise discriminante com mistura de variáveis categóricas e contínuas." Universidade de São Paulo, 1990. http://www.teses.usp.br/teses/disponiveis/45/45133/tde-09112006-094320/.

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O objetivo do trabalho é apresentar os métodos mais consagrados de Análise Discriminante quando temos uma mistura de variáveis categóricas e contínuas.<br>The purpose of this dissertation is to analyze and compare Discriminant Analysis techniques in the presence of mixed categorical and continuous data.

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Semprevivo, Riccardo. "Realization and Performance Characterization of a Myoelectric Control System for Robotic Hands Based on Kernel Ridge Regression." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2019.

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In the field of human-robot interaction, the research is still far to find a solution to a stable control for hand prosthesis. In particular, one of the most promising methodology is represented by the use of electromyographic signals(EMG) of the muscles as a interface between the human and the artificial limb. The EMG is already used to control robotic systems that present a little number of degrees of freedom (d.o.f.), but for more complex controls able to regulate 6 or more hand's degrees of freedom several problems persist. The nonstationarity of the EMG and the nonlinear relation related

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28

Doruska, Paul F. "Methods for Quantitatively Describing Tree Crown Profiles of Loblolly pine (Pinus taeda L.)." Diss., Virginia Tech, 1998. http://hdl.handle.net/10919/30638.

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Physiological process models, productivity studies, and wildlife abundance studies all require accurate representations of tree crowns. In the past, geometric shapes or flexible mathematical equations approximating geometric shapes were used to represent crown profiles. Crown profile of loblolly pine (<I>Pinus taeda</I> L.) was described using single-regressor, nonparametric regression analysis in an effort to improve crown representations. The resulting profiles were compared to more traditional representations. Nonparametric regression may be applicable when an underlying parametric m

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Choy, Kin-yee, and 蔡建怡. "On modelling using radial basis function networks with structure determined by support vector regression." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2004. http://hub.hku.hk/bib/B29329619.

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Gogolashvili, Davit. "Global and local Kernel methods for dataset shift, scalable inference and optimization." Electronic Thesis or Diss., Sorbonne université, 2022. https://accesdistant.sorbonne-universite.fr/login?url=https://theses-intra.sorbonne-universite.fr/2022SORUS363v2.pdf.

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Dans de nombreux problèmes du monde réel, les données de formation et les données de test ont des distributions différentes. Cette situation est communément appelée " décalage de l'ensemble de données ". Les paramètres les plus courants pour le décalage des ensembles de données souvent considérés dans la littérature sont le décalage des covariables et le décalage des cibles. Dans cette thèse, nous étudions les modèles nonparamétriques appliqués au scénario de changement d'ensemble de données. Nous développons un nouveau cadre pour accélérer la régression par processus gaussien. En particulier,

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Chu, Chi-Yang. "Applied Nonparametric Density and Regression Estimation with Discrete Data| Plug-In Bandwidth Selection and Non-Geometric Kernel Functions." Thesis, The University of Alabama, 2017. http://pqdtopen.proquest.com/#viewpdf?dispub=10262364.

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<p> Bandwidth selection plays an important role in kernel density estimation. Least-squares cross-validation and plug-in methods are commonly used as bandwidth selectors for the continuous data setting. The former is a data-driven approach and the latter requires <i>a priori</i> assumptions about the unknown distribution of the data. A benefit from the plug-in method is its relatively quick computation and hence it is often used for preliminary analysis. However, we find that much less is known about the plug-in method in the discrete data setting and this motivates us to propose a plug-in ban

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Santin, Gabriele. "Approximation in kernel-based spaces, optimal subspaces and approximation of eigenfunctions." Doctoral thesis, Università degli studi di Padova, 2016. http://hdl.handle.net/11577/3424498.

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Kernel-based approximation methods provide optimal recovery procedures in the native Hilbert spaces in which they are reproducing. Among other, kernels in the notable class of continuous and strictly positive definite kernels on compact sets possess a series decomposition in L2 - orthonormal eigenfunctions of a particular integral operator. The interest for this decomposition is twofold. On one hand, the subspaces generated by eigenfunctions, or eigenbasis elements, are L2 -optimal trial spaces in the sense of widths. On the other hand, such expansion is the fundamental tool of some of the st

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Xiang, Sijia. "Semiparametric mixture models." Diss., Kansas State University, 2014. http://hdl.handle.net/2097/17338.

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Doctor of Philosophy<br>Department of Statistics<br>Weixin Yao<br>This dissertation consists of three parts that are related to semiparametric mixture models. In Part I, we construct the minimum profile Hellinger distance (MPHD) estimator for a class of semiparametric mixture models where one component has known distribution with possibly unknown parameters while the other component density and the mixing proportion are unknown. Such semiparametric mixture models have been often used in biology and the sequential clustering algorithm. In Part II, we propose a new class of semiparametric

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Linton, Thomas. "Forecasting hourly electricity consumption for sets of households using machine learning algorithms." Thesis, KTH, Skolan för informations- och kommunikationsteknik (ICT), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-186592.

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To address inefficiency, waste, and the negative consequences of electricity generation, companies and government entities are looking to behavioural change among residential consumers. To drive behavioural change, consumers need better feedback about their electricity consumption. A monthly or quarterly bill provides the consumer with almost no useful information about the relationship between their behaviours and their electricity consumption. Smart meters are now widely dispersed in developed countries and they are capable of providing electricity consumption readings at an hourly resolutio

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Kemerling, Robert Alan. "Controlling with Model Trees." NSUWorks, 2011. http://nsuworks.nova.edu/gscis_etd/193.

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This dissertation develops a method of control for nonlinear processes based on regression trees with kernel regression at the leaves as a general control methodology. This methodology offers the ability to control a wide variety of processes exhibiting nonlinear behavior. It takes a place with other machine learning methods that are being applied to nonlinear control, but it does not suffer from the shortcomings of other methods. The method draws on two well-known machine learning methods, regression trees and kernel regression. This dissertation shows that this control method may be programm

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El, Ghouch Anouar. "Nonparametric statistical inference for dependent censored data." Université catholique de Louvain, 2007. http://edoc.bib.ucl.ac.be:81/ETD-db/collection/available/BelnUcetd-09262007-123927/.

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A frequent problem that appears in practical survival data analysis is censoring. A censored observation occurs when the observation of the event time (duration or survival time) may be prevented by the occurrence of an earlier competing event (censoring time). Censoring may be due to different causes. For example, the loss of some subjects under study, the end of the follow-up period, drop out or the termination of the study and the limitation in the sensitivity of a measurement instrument. The literature about censored data focuses on the i.i.d. case. However in many real applications the da

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Lambert, Alex. "Learning function-valued functions in reproducible kernel Hilbert spaces with integral losses : Application to infinite task learning." Electronic Thesis or Diss., Institut polytechnique de Paris, 2021. http://www.theses.fr/2021IPPAT016.

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Les méthodes à noyaux sont au coeur de l'apprentissage statistique. Elles permettent de modéliser des fonctions à valeurs réelles dans des espaces de fonctions à fort potentiel représentatif, sur lesquels la minimisation de risques empiriques régularisés est possible et produit des estimateurs dont le comportement statistique est largement étudié. Lorsque les sorties ne sont plus réelles mais de plus grande dimension, les Espaces de Hilbert à Noyaux Reproduisants à valeurs vectorielles (vv-RKHSs) basés sur des Noyaux à Valeurs Opérateurs (OVKs) fournissent des espaces de fonctions similaires e

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Arale, Brännvall Marian. "Accelerating longitudinal spinfluctuation theory for iron at high temperature using a machine learning method." Thesis, Linköpings universitet, Teoretisk Fysik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-170314.

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In the development of materials, the understanding of their properties is crucial. For magnetic materials, magnetism is an apparent property that needs to be accounted for. There are multiple factors explaining the phenomenon of magnetism, one being the effect of vibrations of the atoms on longitudinal spin fluctuations. This effect can be investigated by simulations, using density functional theory, and calculating energy landscapes. Through such simulations, the energy landscapes have been found to depend on the magnetic background and the positions of the atoms. However, when simulating a s

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Rasteiro, Louise Rossi. "Regressão quantílica para dados censurados." Universidade de São Paulo, 2017. http://www.teses.usp.br/teses/disponiveis/45/45133/tde-09072017-141021/.

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A regressão quantílica para dados censurados é uma extensão dos modelos de regressão quantílica que, por levar em consideração a informação das observações censuradas na modelagem, e por apresentar propriedades bastante satisfatórias, pode ser vista como uma abordagem complementar às metodologias tradicionais em Análise de Sobrevivência, com a vantagem de permitir que as conclusões inferenciais sejam tomadas facilmente em relação aos tempos de sobrevivência propriamente ditos, e não em relação à taxa de riscos ou a uma função desse tempo. Além disso, em alguns casos, pode ser vista também como

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Piccini, Jacopo. "Data Dependent Convergence Guarantees for Regression Problems in Neural Networks." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/24218/.

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It has been recently demonstrated that the artificial neural networks’ (ANN) learning under gradient descent method, can be studied using neural tangent kernel (NTK). This thesis’ goal is to show how techniques related to control theory, can be applied to model and improve the hyperparameters training dynamics. Moreover, it will be proven how by using methods from linear parameter varying (LPV) theory can allow the exact representation of the learning dynamics over its whole domain. The first part of the thesis is dedicated to the modelling and analysis of the system. The modelling of simple

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Wang, Tianyi. "Trajectory Similarity Based Prediction for Remaining Useful Life Estimation." University of Cincinnati / OhioLINK, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1282574910.

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Singh, Yuvraj. "Regression Models to Predict Coastdown Road Load for Various Vehicle Types." The Ohio State University, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=osu1595265184541326.

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Song, Song. "Confidence bands in quantile regression and generalized dynamic semiparametric factor models." Doctoral thesis, Humboldt-Universität zu Berlin, Wirtschaftswissenschaftliche Fakultät, 2010. http://dx.doi.org/10.18452/16341.

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In vielen Anwendungen ist es notwendig, die stochastische Schwankungen der maximalen Abweichungen der nichtparametrischen Schätzer von Quantil zu wissen, zB um die verschiedene parametrische Modelle zu überprüfen. Einheitliche Konfidenzbänder sind daher für nichtparametrische Quantil Schätzungen der Regressionsfunktionen gebaut. Die erste Methode basiert auf der starken Approximation der empirischen Verfahren und Extremwert-Theorie. Die starke gleichmäßige Konsistenz liegt auch unter allgemeinen Bedingungen etabliert. Die zweite Methode beruht auf der Bootstrap Resampling-Verfahren. Es ist bew

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Souza, Roberto Carlos Soares Nalon Pereira. "Algoritmos online baseados em vetores suporte para regressão clássica e ortogonal." Universidade Federal de Juiz de Fora (UFJF), 2013. https://repositorio.ufjf.br/jspui/handle/ufjf/4789.

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Yang, Junjie. "Enhancing surrogate regression methods for structured prediction : An odyssey with loss functions." Electronic Thesis or Diss., Institut polytechnique de Paris, 2025. http://www.theses.fr/2025IPPAT014.

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L'apprentissage automatique, un domaine en rapide évolution à l'intersection des mathématiques et de l'informatique, a transformé à la fois la recherche scientifique et les applications du monde réel. Au-delà de la classification et de la régression, il permet désormais d'aborder la prédiction structurée, permettant des avancées majeures dans la traduction automatique, l'identification des métabolites et la prédiction de la structure des protéines, pour ne citer que quelques exemples. La prédiction structurée (SP) est un défi en raison de son vaste espace de sortie combinatoire. Les méthodes d

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46

Zhai, Jing. "Efficient Exact Tests in Linear Mixed Models for Longitudinal Microbiome Studies." Thesis, The University of Arizona, 2016. http://hdl.handle.net/10150/612412.

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Microbiome plays an important role in human health. The analysis of association between microbiome and clinical outcome has become an active direction in biostatistics research. Testing the microbiome effect on clinical phenotypes directly using operational taxonomic unit abundance data is a challenging problem due to the high dimensionality, non-normality and phylogenetic structure of the data. Most of the studies only focus on describing the change of microbe population that occur in patients who have the specific clinical condition. Instead, a statistical strategy utilizing distance-based o

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47

Fulkerson, Matthew D. "Gas Sensor Array Modeling and Cuprate Superconductivity From Correlated Spin Disorder." The Ohio State University, 2002. http://rave.ohiolink.edu/etdc/view?acc_num=osu1023379849.

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48

Wishart, Justin Rory. "Nonparametric estimation of change-points in derivatives." Thesis, The University of Sydney, 2011. http://hdl.handle.net/2123/8754.

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In this thesis, the main concern is to analyse change-points in a non-parametric regression model. More specifically, the analysis is focussed on the estimation of the location of jumps in the first derivative of the regression function. These change-points will be referred to as kinks. The estimation method is closely based on the zero-crossing technique (ZCT) introduced by Goldenshluger, Tsybakov and Zeevi (2006). The work of Goldenshluger et al. (2006) was aimed at estimating jumps in the regression function in the indirect non-parametric regression model and shown to be optimal in the min

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Fan, Liangdong. "ESTIMATION IN PARTIALLY LINEAR MODELS WITH CORRELATED OBSERVATIONS AND CHANGE-POINT MODELS." UKnowledge, 2018. https://uknowledge.uky.edu/statistics_etds/32.

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Methods of estimating parametric and nonparametric components, as well as properties of the corresponding estimators, have been examined in partially linear models by Wahba [1987], Green et al. [1985], Engle et al. [1986], Speckman [1988], Hu et al. [2004], Charnigo et al. [2015] among others. These models are appealing due to their flexibility and wide range of practical applications including the electricity usage study by Engle et al. [1986], gum disease study by Speckman [1988], etc., wherea parametric component explains linear trends and a nonparametric part captures nonlinear relationshi

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Batistão, Mariana Dias Chaves [UNESP]. "Proposta metodológica para identificar fatores contribuintes de acidentes viários por meio de geotecnologias." Universidade Estadual Paulista (UNESP), 2018. http://hdl.handle.net/11449/152750.

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