| Description |
1 online resource (308 pages) |
| Contents |
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 |
|
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 |
|
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 |
|
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 |
|
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 |
| 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 sparseness and employing robust statistics. The framework is further extended towards unsupervised learning by considering PCA |
| Bibliography |
Includes bibliographical references (pages 269-286) and index |
| Notes |
Print version record |
| Subject |
Algorithms.
|
|
Kernel functions.
|
|
Least squares.
|
|
Machine learning.
|
|
Algorithms
|
|
Machine Learning
|
|
algorithms.
|
|
Algorithms
|
|
Kernel functions
|
|
Least squares
|
|
Machine learning
|
| Form |
Electronic book
|
| Author |
Gestel, Tony van
|
|
De Brabanter, Jos
|
|
De Moor, Bart
|
|
Vandewalle, Joos
|
| ISBN |
9789812776655 |
|
9812776656 |
|
