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Record Nr. |
UNINA9910777501403321 |
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Titolo |
Least squares support vector machines [[electronic resource] /] / Johan A.K. Suykens ... [et al.] |
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Pubbl/distr/stampa |
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River Edge, NJ, : World Scientific, 2002 |
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ISBN |
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Descrizione fisica |
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1 online resource (308 p.) |
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Altri autori (Persone) |
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Disciplina |
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Soggetti |
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Machine learning |
Algorithms |
Kernel functions |
Least squares |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Description based upon print version of record. |
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Nota di bibliografia |
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Includes bibliographical references and index. |
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Nota di contenuto |
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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 |
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