Least squares support vector machines

著者

書誌事項

Least squares support vector machines

Johan A. K. Suykens ... [et al.]

World Scientific, c2002

大学図書館所蔵 件 / 全14件

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注記

Includes bibliographical references and index

内容説明・目次

内容説明

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 Nystroem sampling with active selection of support vectors. The methods are illustrated with several examples.

目次

  • Support Vector Machines
  • Basic Methods of Least Squares Support Vector Machines
  • Bayesian Inference for LS-SVM Models
  • Robustness
  • Large Scale Problems
  • LS-SVM for Unsupervised Learning
  • LS-SVM for Recurrent Networks and Control.

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