ISBN: 9789812381514 - least squares support vector machines (17 risultati)

Hardcover. Condizione: Very Good. With dust jacket. It's a well-cared-for item that has seen limited use. The item may show minor signs of wear. All the text is legible, with all pages included. It may have slight markings and/or highlighting.

Condizione: good. A copy that has been read, remains in good condition. All pages are intact, and the cover is intact. The spine and cover show signs of wear. Pages can include notes and highlighting and show signs of wear, and the copy can include "From the library of" labels or previous owner inscriptions. 100% GUARANTEE! Shipped with delivery confirmation, if you're not satisfied with purchase please return item! Ships via media mail.…

hardcover. Condizione: Good. No dust jacket. Ex-library book with typical stickers and stampings. Priority Mail is available on this item. No international shipping.

hardcover. Condizione: Very Good. Yellowed dust jacket. Priority Mail is available on this item. No international shipping.

Condizione: New.

HRD. Condizione: New. New Book. Shipped from UK. Established seller since 2000.

Condizione: New.

Condizione: As New. Unread book in perfect condition.

HRD. Condizione: New. New Book. Shipped from UK. Established seller since 2000.

Condizione: New.

Condizione: As New. Unread book in perfect condition.

Condizione: New. In English.

Hardcover. Condizione: Brand New. 308 pages. 9.25x6.25x1.00 inches. In Stock.

Condizione: Very Good.

hardcover. Condizione: New. In shrink wrap. Looks like an interesting title.

Gebunden. Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. An examination of least squares support vector machines (LS-SVMs) which are reformulations to standard SVMs. The authors explain the natural links between LS-SVM classifiers and kernel Fisher discriminant analysis. Bayesian inference of LS-SVM models is als.…

Buch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 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.…