Least Squares Support Vector Machines is popular PDF and ePub book, written by Johan A. K. Suykens in 2002, it is a fantastic choice for those who relish reading online the Mathematics genre. Let's immerse ourselves in this engaging Mathematics book by exploring the summary and details provided below. Remember, Least Squares Support Vector Machines can be Read Online from any device for your convenience.
Least Squares Support Vector Machines Book PDF Summary
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 spareness 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 Nystrom sampling with active selection of support vectors. The methods are illustrated with several examples.
Detail Book of Least Squares Support Vector Machines PDF
- Author : Johan A. K. Suykens
- Release : 21 September 2024
- Publisher : World Scientific
- ISBN : 9812381511
- Genre : Mathematics
- Total Page : 318 pages
- Language : English
- PDF File Size : 17,5 Mb
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