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

Edité par World Scientific Pub Co Inc, 2002

A propos de cet article

N° de réf. du vendeur ABLIING23Apr0412070094793

Détails bibliographiques

• Titre: Least Squares Support Vector Machines
• Éditeur: World Scientific Pub Co Inc
• Date de parution: 2002
• Langue: anglais
• ISBN à 10 chiffres: 9812381511
• ISBN à 13 chiffres: 9789812381514
• Reliure: Couverture rigide
• État de l'article: New

A propos de ce titre

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.