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LEAST SQUARES SUPPORT VECTOR MACHINES

Product Image: LEAST SQUARES SUPPORT VECTOR MACHINES

LEAST SQUARES SUPPORT VECTOR MACHINES

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Description
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 spareness 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 Nystrom sampling with active selection of support vectors. The methods are illustrated with several examples.
Product details
Number of Pages:
310
Release Date:
2002-11-14
Publication Date:
2002-11-12
Publisher:
World Scientific
Languages:
Original: English
ISBN10:
9812381511
ISBN13:
9789812381514
Weight:
602 g
Height:
157 cm
Width:
235 cm
Thickness:
21 cm

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