Least squares support vector machines / Johan A.K. Suykens ... [and others].

Format:

Book

Contributor:

Suykens, Johan A. K.

Language:

English

Subjects (All):

Machine learning.

Algorithms.

Kernel functions.

Least squares.

Physical Description:

xiv, 294 pages : illustrations ; 24 cm

Place of Publication:

River Edge, NJ : World Scientific, [2002]

Summary:

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.

Notes:

Includes bibliographical references (pages 269-286) and index.

ISBN:

9812381511

OCLC:

51312978