Least Squares Support Vector Machines by SuykensLeast Squares Support Vector Machines by Suykens

Least Squares Support Vector Machines

bySuykens

Hardcover | November 12, 2002

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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.
Title:Least Squares Support Vector MachinesFormat:HardcoverDimensions:9.41 × 7.24 × 0.98 inPublished:November 12, 2002Publisher:World Scientific PublishingLanguage:English

The following ISBNs are associated with this title:

ISBN - 10:9812381511

ISBN - 13:9789812381514

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