Least Squares Support Vector Machines
Joseph De Brabanter, Bart De Moor, Johan A K Suykens
- 308 pagine
- English
- PDF
- Disponibile su iOS e Android
Least Squares Support Vector Machines
Joseph De Brabanter, Bart De Moor, Johan A K Suykens
Informazioni sul libro
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.
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Indice dei contenuti
- Contents
- Preface
- Chapter 1 Introduction
- Chapter 2 Support Vector Machines
- Chapter 3 Basic Methods of Least Squares Support Vector Machines
- Chapter 4 Bayesian Inference for LS-SVM Models
- Chapter 5 Robustness
- Chapter 6 Large Scale Problems
- Chapter 7 LS-SVM for Unsupervised Learning
- Chapter 8 LS-SVM for Recurrent Networks and Control
- Appendix A
- Bibliography
- List of Symbols
- Acronyms
- Index