LS-SVMs: book announcement

[Johan Suykens]

We are glad to announce the publication of a new book

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J.A.K. Suykens, T. Van Gestel, J. De Brabanter, B. De Moor, J. Vandewalle,
Least Squares Support Vector Machines,
World Scientific Pub. Co., Singapore, 2002 (ISBN 981-238-151-1)
http://www.esat.kuleuven.ac.be/sista/lssvmlab/book.html

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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 Nystr=F6m sampling
with active selection of support vectors. The methods are illustrated
with several examples.


Contents:
 Introduction
 Support vector machines
 Least squares support vector machines, links with Gaussian
  processes, regularization networks, and kernel FDA
 Bayesian inference for LS-SVM models
 Weighted versions and robust statistics
 Large scale problems: Nystrom sampling, reduced set methods,
  basis formation and Fixed size LS-SVM
 LS-SVM for unsupervised learning: support vector machines
  formulations for kernel PCA. Related methods of kernel CCA.
 LS-SVM for recurrent networks and control
 Illustrations and applications


Readership:
Graduate students and researchers in neural networks; machine learning;
data-mining; signal processing; circuit, systems and control theory;
pattern recognition; and statistics.


Info: 308pp., Publication date: Nov. 2002,
ISBN 981-238-151-1

Order information: World Scientific
http://www.wspc.com/books/compsci/5089.html
http://www.esat.kuleuven.ac.be/sista/lssvmlab/book.html

Freely available LS-SVMlab software
http://www.esat.kuleuven.ac.be/sista/lssvmlab/
under GNU General Public License


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