Least Squares Support Vector Machines

Least Squares Support Vector Machines

by Johan A. K. Suykens, J. A. K. Suykens, T. Van Gestel
     
 

ISBN-10: 9812381511

ISBN-13: 9789812381514

Pub. Date: 11/28/2002

Publisher: World Scientific Publishing Company, Incorporated

This book focuses on Least Squares Support Vector Machines (LS-SVMs) which are reformulations to standard SVMs. LS-SVMs are closely related to regularization networks and Gaussian processes but additionally emphasize and exploit primal-dual interpretations from optimization theory. The authors explain the natural links between LS-SVM classifiers and kernel Fisher…  See more details below

Overview

This book focuses on Least Squares Support Vector Machines (LS-SVMs) which are reformulations to standard SVMs. LS-SVMs are closely related to regularization networks and Gaussian processes but additionally emphasize and exploit primal-dual interpretations from optimization theory. The authors explain the natural links between LS-SVM classifiers and kernel Fisher discriminant analysis. Bayesian inference of LS-SVM models is discussed, together with methods for imposing sparseness and employing robust statistics.The framework is further extended towards unsupervised learning by considering PCA analysis and its kernel version as a one-class modelling problem. This leads to new primal-dual support vector machine formulations for kernel PCA and kernel CCA analysis. Furthermore, LS-SVM formulations are given for recurrent networks and control. In general, support vector machines may pose heavy computational challenges for large data sets. For this purpose, a method of fixed size LS-SVM is proposed where the estimation is done in the primal space in relation to a Nyström sampling with active selection of support vectors. The methods are illustrated with several examples.

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Product Details

ISBN-13:
9789812381514
Publisher:
World Scientific Publishing Company, Incorporated
Publication date:
11/28/2002
Pages:
308
Product dimensions:
5.90(w) x 9.10(h) x 1.00(d)

Table of Contents

Ch. 1Introduction1
Ch. 2Support Vector Machines29
Ch. 3Basic Methods of Least Squares Support Vector Machines71
Ch. 4Bayesian Inference for LS-SVM Models117
Ch. 5Robustness149
Ch. 6Large Scale Problems173
Ch. 7LS-SVM for Unsupervised Learning201
Ch. 8LS-SVM for Recurrent Networks and Control225
App. A249
Bibliography269
List of Symbols287
Acronyms289
Index291

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