Least Squares Support Vector Machines

Joseph De Brabanter, Bart De Moor, Johan A K Suykens

• Publisher: World Scientific
• Year: 2002
• Print ISBN: 9789812381514, 9789812381514
• eBook ISBN: 9789812776655
• Topic: Ciencia de la computación
• Subtopic: Redes neuronales

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.

Table of contents

• Contents
• Preface
• Chapter 1 Introduction
• Chapter 2 Support Vector Machines
• Chapter 3 Basic Methods of Least Squares Support Vector Machines
• Chapter 4 Bayesian Inference for LS-SVM Models
• Chapter 5 Robustness
• Chapter 6 Large Scale Problems
• Chapter 7 LS-SVM for Unsupervised Learning
• Chapter 8 LS-SVM for Recurrent Networks and Control
• Appendix A
• Bibliography
• List of Symbols
• Acronyms
• Index