Least Squares Support Vector Machines
Regular price
€112.99
14 days return policy
Shipping & Delivery
A01=Bart De Moor
A01=Johan A K Suykens
A01=Joos P L Vandewalle
A01=Joseph De Brabanter
A01=Tony Van Gestel
Author_Bart De Moor
Author_Johan A K Suykens
Author_Joos P L Vandewalle
Author_Joseph De Brabanter
Author_Tony Van Gestel
Category=UYQM
Control Theory
eq_bestseller
eq_computing
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
eq_non-fiction
Neural Networks/Machine Learning
Neural NetworksMachine Learning
Support Vector Machines
Product details
- ISBN 9789812381514
- Publication Date: 14 Nov 2002
- Publisher: World Scientific Publishing Co Pte Ltd
- Publication City/Country: SG
- Product Form: Hardback
Secure
checkout
Fast Shipping
Easy returns
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.
