Least Squares Support Vector Machines

Johan A K Suykens; Tony Van Gestel; Jos De Brabanter; Bart De Moor; Joos Vandewalle

ISBN 10: 9812381511 / ISBN 13: 9789812381514

Published by World Scientific Pub Co Inc, 2002

About this Item

Yellowed dust jacket. Priority Mail is available on this item. No international shipping.

Seller Inventory # A180425mug172468

Bibliographic Details

• Title: Least Squares Support Vector Machines
• Publisher: World Scientific Pub Co Inc
• Publication Date: 2002
• Language: English
• ISBN 10: 9812381511
• ISBN 13: 9789812381514
• Binding: hardcover
• Condition: Very Good
• Weight: 20 ounces

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.