Least Squares Support Vector Machines

Least Squares Support Vector Machines

By: Joos Vandewalle (author), Johan A. K. Suykens (author), Joseph de Brabanter (author), Bart De Moor (author), Eng Huay (volume_editor)Hardback

Up to 2 WeeksUsually despatched within 2 weeks


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.


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.

Product Details

  • ISBN13: 9789812381514
  • Format: Hardback
  • Number Of Pages: 308
  • ID: 9789812381514
  • ISBN10: 9812381511

Delivery Information

  • Saver Delivery: Yes
  • 1st Class Delivery: Yes
  • Courier Delivery: Yes
  • Store Delivery: Yes

Prices are for internet purchases only. Prices and availability in WHSmith Stores may vary significantly