Least Squares Support Vector Machines
| AUTHOR | Van Gestel, Tony; Suykens, Johan A. K.; Van Gestel, Tony et al. |
| PUBLISHER | World Scientific Publishing Company (11/14/2002) |
| PRODUCT TYPE | Hardcover (Hardcover) |
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 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 Nystr m sampling with active selection of support vectors. The methods are illustrated with several examples.
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Product Format
Product Details
ISBN-13:
9789812381514
ISBN-10:
9812381511
Binding:
Hardback or Cased Book (Sewn)
Content Language:
English
More Product Details
Page Count:
308
Carton Quantity:
22
Product Dimensions:
6.52 x 0.84 x 9.48 inches
Weight:
1.23 pound(s)
Country of Origin:
SG
Subject Information
BISAC Categories
Computers | Data Science - Neural Networks
Computers | Artificial Intelligence - General
Computers | Networking - General
Dewey Decimal:
006
Library of Congress Control Number:
2002033063
Descriptions, Reviews, Etc.
publisher marketing
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 Nystr m sampling with active selection of support vectors. The methods are illustrated with several examples.
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