Least Squares Support Vector Machines.
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 expla...
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| Format: | Electronic eBook |
| Language: | English |
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Singapore :
World Scientific Publishing Company,
2002
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| Local Note: | ProQuest Ebook Central |
MARC
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| 100 | 1 | |a Suykens, Johan A. K. | |
| 245 | 1 | 0 | |a Least Squares Support Vector Machines. |
| 260 | |a Singapore : |b World Scientific Publishing Company, |c 2002. | ||
| 300 | |a 1 online resource (308 pages) | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 504 | |a Includes bibliographical references (pages 269-286) and index. | ||
| 505 | 0 | |a Preface ; Chapter 1 Introduction ; 1.1 Multilayer perceptron neural networks ; 1.2 Regression and classification ; 1.3 Learning and generalization ; 1.3.1 Weight decay and effective number of parameters ; 1.3.2 Ridge regression ; 1.3.3 Bayesian learning. | |
| 505 | 8 | |a 1.4 Principles of pattern recognition 1.4.1 Bayes rule and optimal classifier under Gaussian assumptions ; 1.4.2 Receiver operating characteristic ; 1.5 Dimensionality reduction methods ; 1.6 Parametric versus non-parametric approaches and RBF networks. | |
| 505 | 8 | |a 1.7 Feedforward versus recurrent network models Chapter 2 Support Vector Machines ; 2.1 Maximal margin classification and linear SVMs ; 2.1.1 Margin ; 2.1.2 Linear SVM classifier: separable case ; 2.1.3 Linear SVM classifier: non-separable case ; 2.2 Kernel trick and Mercer condition. | |
| 505 | 8 | |a 2.3 Nonlinear SVM classifiers 2.4 VC theory and structural risk minimization ; 2.4.1 Empirical risk versus generalization error ; 2.4.2 Structural risk minimization ; 2.5 SVMs for function estimation ; 2.5.1 SVM for linear function estimation. | |
| 505 | 8 | |a 2.5.2 SVM for nonlinear function estimation 2.5.3 VC bound on generalization error ; 2.6 Modifications and extensions ; 2.6.1 Kernels ; 2.6.2 Extension to other convex cost functions ; 2.6.3 Algorithms ; 2.6.4 Parametric versus non-parametric approaches. | |
| 520 | |a 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. | ||
| 588 | 0 | |a Print version record. | |
| 590 | |a ProQuest Ebook Central |b Ebook Central Academic Complete | ||
| 650 | 0 | |a Algorithms. | |
| 650 | 0 | |a Kernel functions. | |
| 650 | 0 | |a Least squares. | |
| 650 | 0 | |a Machine learning. | |
| 650 | 2 | |a Algorithms | |
| 650 | 2 | |a Machine Learning | |
| 650 | 7 | |a algorithms. |2 aat | |
| 700 | 1 | |a Gestel, Tony van. | |
| 700 | 1 | |a De Brabanter, Jos. | |
| 700 | 1 | |a De Moor, Bart. | |
| 700 | 1 | |a Vandewalle, Joos. | |
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