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Least Squares Support Vector Machines

by Johan A. K. Suykens , Tony Van Gestel , Joseph De Brabanter
Save 17% Save 17%
Current price ₹11,408.00
Original price ₹13,690.00
Original price ₹13,690.00
Original price ₹13,690.00
(-17%)
₹11,408.00
Current price ₹11,408.00

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Book cover type: Hardcover
  • ISBN13: 9789812381514
  • Binding: Hardcover
  • Subject: N/A
  • Publisher: World Scientific Publishing Company
  • Publisher Imprint: World Scientific Publishing Company
  • Publication Date:
  • Pages: 308
  • Original Price: USD 111.0
  • Language: English
  • Edition: N/A
  • Item Weight: 558 grams
  • BISAC Subject(s): Data Science / Neural Networks

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.

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