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Least Squares Support Vector Machines

By: Bart De Moor, Tony Van Gestel, Jos De Brabanter, Joos Vandewalle, Johan A K Suykens

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Hardcover | 14 November 2002

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Hardcover
310 Pages

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23.4 x 16.0 x 2.1

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An examination of 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 Nystrom sampling with active selection of support vectors. The methods are illustrated with several examples.

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You Can Find This Book In

Non-Fiction Mathematics Probability & Statistics Computing & I.T.Computer Science Artificial Intelligence Machine Learning Neural Networks & Fuzzy Systems Computer Networking & Communications Engineering & Technology Energy Technology & Engineering Electrical Engineering

Booktopia Publisher Services World Scientific Publishing

Introduction	p. 1
Support Vector Machines	p. 29
Basic Methods of Least Squares Support Vector Machines	p. 71
Bayesian Inference for LS-SVM Models	p. 117
Robustness	p. 149
Large Scale Problems	p. 173
LS-SVM for Unsupervised Learning	p. 201
LS-SVM for Recurrent Networks and Control	p. 225
App. A	p. 249
Bibliography	p. 269
List of Symbols	p. 287
Acronyms	p. 289
Index	p. 291
Table of Contents provided by Blackwell. All Rights Reserved.

Least Squares Support Vector Machines

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Hardcover

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