Least Squares Support Vector Machines

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

ISBN 10: 9812381511 / ISBN 13: 9789812381514

Published by World Scientific Publishing Co Pte Ltd, SG, 2002

About this Item

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.

Seller Inventory # LU-9789812381514

Bibliographic Details

• Title: Least Squares Support Vector Machines
• Publisher: World Scientific Publishing Co Pte Ltd, SG
• Publication Date: 2002
• Language: English
• ISBN 10: 9812381511
• ISBN 13: 9789812381514
• Binding: Hardback
• Condition: New
• Dimensions: 15.24W x 1.91H x 22.86D centimeters 15.24 centimeters width by 1.91 centimeters height by 22.86 centimeters depth

About this title

Synopsis

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.