Bayesian Wavelet And Fourier Transform Kernel Regression And Classification In Rkhs
DOWNLOAD
Download Bayesian Wavelet And Fourier Transform Kernel Regression And Classification In Rkhs PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Bayesian Wavelet And Fourier Transform Kernel Regression And Classification In Rkhs book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages. If the content not found or just blank you must refresh this page
Bayesian Wavelet And Fourier Transform Kernel Regression And Classification In Rkhs
DOWNLOAD
Author : Xueying Zhang
language : en
Publisher:
Release Date : 2021
Bayesian Wavelet And Fourier Transform Kernel Regression And Classification In Rkhs written by Xueying Zhang and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021 with Hilbert space categories.
Kernel methods are often used for nonlinear regression and classification in machine learning because they are computationally cheap and accurate. Fourier basis and wavelet basis are the bases that can efficiently approximate the kernel functions. In previous research, Bayesian approximate kernel regression with Fourier transform has been proposed. With the proposed method, we use the analytic properties of the reproducing kernel Hilbert space (RKHS) to define a linear vector space that captures nonlinear structures. We map the data into a low-dimensional randomized feature space using Fourier transform and convert kernel function into operations of a linear machine. A Bayesian approximate kernel regression model is then formulated with the application of a generalized kernel model and the Bayesian method. We replace Fourier transform with wavelet transform in randomized feature space to approximate kernel functions. We formulate a new Bayesian approximate kernel model with wavelet transform and use the Gibbs sampler to compute the parameters of the model. We then make a comparison between the performance of Fourier based and wavelet-based Bayesian approximate kernels solving both regression and classification problems.
Statistical Modeling By Wavelets
DOWNLOAD
Author : Brani Vidakovic
language : en
Publisher: Wiley-Interscience
Release Date : 2013-05-10
Statistical Modeling By Wavelets written by Brani Vidakovic and has been published by Wiley-Interscience this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-05-10 with Mathematics categories.
Statistical Modeling by Wavelets, Second Edition compiles, organizes, and explains research data previously made available only in disparate journal articles. The author carefully balances both statistical and mathematical techniques, supplementing the material with a wealth of examples, more than 100 illustrations, extensive references with data sets, and MatLab® and WaveLab® wavelet overviews made available for downloading over the Internet. Accessible to anyone with a background in advanced calculus and algebra, this book has become the standard reference for statisticians and engineers seeking a comprehensive introduction to an ever-changing field.
Wavelets And Statistics
DOWNLOAD
Author : Anestis Antoniadis
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Wavelets And Statistics written by Anestis Antoniadis and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-06 with Mathematics categories.
Despite its short history, wavelet theory has found applications in a remarkable diversity of disciplines: mathematics, physics, numerical analysis, signal processing, probability theory and statistics. The abundance of intriguing and useful features enjoyed by wavelet and wavelet packed transforms has led to their application to a wide range of statistical and signal processing problems. On November 16-18, 1994, a conference on Wavelets and Statistics was held at Villard de Lans, France, organized by the Institute IMAG-LMC, Grenoble, France. The meeting was the 15th in the series of the Rencontres Pranco-Belges des 8tatisticiens and was attended by 74 mathematicians from 12 different countries. Following tradition, both theoretical statistical results and practical contributions of this active field of statistical research were presented. The editors and the local organizers hope that this volume reflects the broad spectrum of the conference. as it includes 21 articles contributed by specialists in various areas in this field. The material compiled is fairly wide in scope and ranges from the development of new tools for non parametric curve estimation to applied problems, such as detection of transients in signal processing and image segmentation. The articles are arranged in alphabetical order by author rather than subject matter. However, to help the reader, a subjective classification of the articles is provided at the end of the book. Several articles of this volume are directly or indirectly concerned with several as pects of wavelet-based function estimation and signal denoising.
Wavelet Transforms And Their Applications
DOWNLOAD
Author : Lokenath Debnath
language : en
Publisher: Springer
Release Date : 2014-11-25
Wavelet Transforms And Their Applications written by Lokenath Debnath and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-11-25 with Technology & Engineering categories.
This textbook is an introduction to wavelet transforms and accessible to a larger audience with diverse backgrounds and interests in mathematics, science, and engineering. Emphasis is placed on the logical development of fundamental ideas and systematic treatment of wavelet analysis and its applications to a wide variety of problems as encountered in various interdisciplinary areas. Topics and Features: * This second edition heavily reworks the chapters on Extensions of Multiresolution Analysis and Newlands’s Harmonic Wavelets and introduces a new chapter containing new applications of wavelet transforms * Uses knowledge of Fourier transforms, some elementary ideas of Hilbert spaces, and orthonormal systems to develop the theory and applications of wavelet analysis * Offers detailed and clear explanations of every concept and method, accompanied by carefully selected worked examples, with special emphasis given to those topics in which students typically experience difficulty * Includes carefully chosen end-of-chapter exercises directly associated with applications or formulated in terms of the mathematical, physical, and engineering context and provides answers to selected exercises for additional help Mathematicians, physicists, computer engineers, and electrical and mechanical engineers will find Wavelet Transforms and Their Applications an exceptionally complete and accessible text and reference. It is also suitable as a self-study or reference guide for practitioners and professionals.
Bayesian Approach To Inverse Problems
DOWNLOAD
Author : Jérôme Idier
language : en
Publisher: John Wiley & Sons
Release Date : 2013-03-01
Bayesian Approach To Inverse Problems written by Jérôme Idier and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-03-01 with Mathematics categories.
Many scientific, medical or engineering problems raise the issue of recovering some physical quantities from indirect measurements; for instance, detecting or quantifying flaws or cracks within a material from acoustic or electromagnetic measurements at its surface is an essential problem of non-destructive evaluation. The concept of inverse problems precisely originates from the idea of inverting the laws of physics to recover a quantity of interest from measurable data. Unfortunately, most inverse problems are ill-posed, which means that precise and stable solutions are not easy to devise. Regularization is the key concept to solve inverse problems. The goal of this book is to deal with inverse problems and regularized solutions using the Bayesian statistical tools, with a particular view to signal and image estimation. The first three chapters bring the theoretical notions that make it possible to cast inverse problems within a mathematical framework. The next three chapters address the fundamental inverse problem of deconvolution in a comprehensive manner. Chapters 7 and 8 deal with advanced statistical questions linked to image estimation. In the last five chapters, the main tools introduced in the previous chapters are put into a practical context in important applicative areas, such as astronomy or medical imaging.
Pattern Recognition And Machine Learning
DOWNLOAD
Author : Christopher M. Bishop
language : en
Publisher: Springer
Release Date : 2016-08-23
Pattern Recognition And Machine Learning written by Christopher M. Bishop and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-08-23 with Computers categories.
This is the first textbook on pattern recognition to present the Bayesian viewpoint. The book presents approximate inference algorithms that permit fast approximate answers in situations where exact answers are not feasible. It uses graphical models to describe probability distributions when no other books apply graphical models to machine learning. No previous knowledge of pattern recognition or machine learning concepts is assumed. Familiarity with multivariate calculus and basic linear algebra is required, and some experience in the use of probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.
Oracle Inequalities In Empirical Risk Minimization And Sparse Recovery Problems
DOWNLOAD
Author : Vladimir Koltchinskii
language : en
Publisher: Springer
Release Date : 2011-07-29
Oracle Inequalities In Empirical Risk Minimization And Sparse Recovery Problems written by Vladimir Koltchinskii and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-07-29 with Mathematics categories.
The purpose of these lecture notes is to provide an introduction to the general theory of empirical risk minimization with an emphasis on excess risk bounds and oracle inequalities in penalized problems. In recent years, there have been new developments in this area motivated by the study of new classes of methods in machine learning such as large margin classification methods (boosting, kernel machines). The main probabilistic tools involved in the analysis of these problems are concentration and deviation inequalities by Talagrand along with other methods of empirical processes theory (symmetrization inequalities, contraction inequality for Rademacher sums, entropy and generic chaining bounds). Sparse recovery based on l_1-type penalization and low rank matrix recovery based on the nuclear norm penalization are other active areas of research, where the main problems can be stated in the framework of penalized empirical risk minimization, and concentration inequalities and empirical processes tools have proved to be very useful.
Automatic Speech And Speaker Recognition
DOWNLOAD
Author : Joseph Keshet
language : en
Publisher: John Wiley & Sons
Release Date : 2009-04-27
Automatic Speech And Speaker Recognition written by Joseph Keshet and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-04-27 with Technology & Engineering categories.
This book discusses large margin and kernel methods for speech and speaker recognition Speech and Speaker Recognition: Large Margin and Kernel Methods is a collation of research in the recent advances in large margin and kernel methods, as applied to the field of speech and speaker recognition. It presents theoretical and practical foundations of these methods, from support vector machines to large margin methods for structured learning. It also provides examples of large margin based acoustic modelling for continuous speech recognizers, where the grounds for practical large margin sequence learning are set. Large margin methods for discriminative language modelling and text independent speaker verification are also addressed in this book. Key Features: Provides an up-to-date snapshot of the current state of research in this field Covers important aspects of extending the binary support vector machine to speech and speaker recognition applications Discusses large margin and kernel method algorithms for sequence prediction required for acoustic modeling Reviews past and present work on discriminative training of language models, and describes different large margin algorithms for the application of part-of-speech tagging Surveys recent work on the use of kernel approaches to text-independent speaker verification, and introduces the main concepts and algorithms Surveys recent work on kernel approaches to learning a similarity matrix from data This book will be of interest to researchers, practitioners, engineers, and scientists in speech processing and machine learning fields.
Generalized Mercer Kernels And Reproducing Kernel Banach Spaces
DOWNLOAD
Author : Yuesheng Xu
language : en
Publisher: American Mathematical Soc.
Release Date : 2019-04-10
Generalized Mercer Kernels And Reproducing Kernel Banach Spaces written by Yuesheng Xu and has been published by American Mathematical Soc. this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-04-10 with Banach spaces categories.
This article studies constructions of reproducing kernel Banach spaces (RKBSs) which may be viewed as a generalization of reproducing kernel Hilbert spaces (RKHSs). A key point is to endow Banach spaces with reproducing kernels such that machine learning in RKBSs can be well-posed and of easy implementation. First the authors verify many advanced properties of the general RKBSs such as density, continuity, separability, implicit representation, imbedding, compactness, representer theorem for learning methods, oracle inequality, and universal approximation. Then, they develop a new concept of generalized Mercer kernels to construct p-norm RKBSs for 1≤p≤∞ .
Artificial Intelligence And Statistics
DOWNLOAD
Author : William A. Gale
language : en
Publisher: Addison Wesley Publishing Company
Release Date : 1986
Artificial Intelligence And Statistics written by William A. Gale and has been published by Addison Wesley Publishing Company this book supported file pdf, txt, epub, kindle and other format this book has been release on 1986 with Computers categories.
A statistical view of uncertainty in expert systems. Knowledge, decision making, and uncertainty. Conceptual clustering and its relation to numerical taxonomy. Learning rates in supervised and unsupervised intelligent systems. Pinpoint good hypotheses with heuristics. Artificial intelligence approaches in statistics. REX review. Representing statistical computations: toward a deeper understanding. Student phase 1: a report on work in progress. Representing statistical knowledge for expert data analysis systems. Environments for supporting statistical strategy. Use of psychometric tools for knowledge acquisition: a case study. The analysis phase in development of knowledge based systems. Implementation and study of statistical strategy. Patterns in statisticalstrategy. A DIY guide to statistical strategy. An alphabet for statistician's expert systems.