Least Squares Support Vector Machines

di Johan A K Suykens (Autore)

Tony Van Gestel (Autore)

Joseph De Brabanter (Autore)

World Scientific Publishing Co Pte Ltd, 2002

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.

• Autore: Johan A K Suykens, Tony Van Gestel, Joseph De Brabanter
• Editore: World Scientific Publishing Co Pte Ltd
• Anno: 2002
• Rilegatura: Hardback
• Pagine: 308 p.
• EAN: 9789812381514

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