Least Squares Support Vector Machines

by Johan A K Suykens, Tony Van Gestel, Joseph De Brabanter, Bart De Moor, Joos P L Vandewalle

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.

Product Details

ISBN-13:	9789812381514
Publisher:	World Scientific Publishing Company, Incorporated
Publication date:	11/14/2002
Pages:	308
Product dimensions:	5.90(w) x 9.10(h) x 1.00(d)