Least Squares Support Vector Machines (Hardcover) (ISBN-13: 9789812381514)

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 Nystr m sampling with active selection of support vectors. The methods are illustrated with several examples.

Author: Johan A. K. Suykens, Tony Van Gestel, Joseph De Brabanter
ISBN-10: 9812381511
ISBN-13: 9789812381514
Publisher: World Scientific Publishing Company
Language: English
Published: 11/14/2002
Pages: 308
Format: Hardcover
Weight: 1.23lbs
Size: 9.48h x 6.52w x 0.84d