Least Squares Support Vector Machines

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 spareness 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.

List of contents

Support Vector Machines; Basic Methods of Least Squares Support Vector Machines; Bayesian Inference for LS-SVM Models; Robustness; Large Scale Problems; LS-SVM for Unsupervised Learning; LS-SVM for Recurrent Networks and Control.

Product details

• Authors: J. De Brabanter, Joseph De Brabanter, Bart De Moor, T. van Gestel, Tony van Gestel, Et Al Johan A K Suykens, J. A. K. Suykens, Johan A K Suykens, Johan A. K. Suykens, T. Van Gestel, Joos P L Vandewalle
• Publisher: World Scientific
• Languages: English
• Product format: Hardback
• Released: 12.11.2002
• EAN: 9789812381514
• ISBN: 978-981-238-151-4
• No. of pages: 310
• Dimensions: 157 mm x 235 mm x 21 mm
• Weight: 602 g
• Subject: Natural sciences, medicine, IT, technology > IT, data processing > IT