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[null,"1.","Record Nr.",null,"UNINA9910777501403321"]
[null,null,"Titolo",null,"Least squares support vector machines [[electronic resource] /] / Johan A.K. Suykens ... [et al.]"]
[null,null,"Pubbl/distr/stampa",null,"River Edge, NJ, : World Scientific, 2002"]
[null,null,"ISBN",null,"981-277-665-6"]
[null,null,"Descrizione fisica",null,"1 online resource (308 p.)"]
[null,null,"Disciplina",null,"006.3/1"]
[null,null,"Soggetti",null,"Machine learning"]
[{"$nulls":4},"Algorithms"]
[{"$nulls":4},"Kernel functions"]
[{"$nulls":4},"Least squares"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","Description based upon print version of record."]
[null,null,null,"Nota di bibliografia","Includes bibliographical references and index."]
[null,null,"Nota di contenuto",null,"Contents               ; Preface              ; Chapter 1 Introduction                             ; 1.1 Multilayer perceptron neural networks                                                ; 1.2 Regression and classification                                        ; 1.3 Learning and generalization                                      ; 1.3.1 Weight decay and effective number of parameters                                                            ; 1.3.2 Ridge regression                             ; 1.3.3 Bayesian learning"]
[{"$nulls":4},"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"]
[{"$nulls":4},"1.7 Feedforward versus recurrent network models                                                      Chapter 2 Support Vector Machines                                        ; 2.1 Maximal margin classification and linear SVMs                                                        ; 2.1.1 Margin                   ; 2.1.2 Linear SVM classifier: separable case                                                  ; 2.1.3 Linear SVM classifier: non-separable case                                                      ; 2.2 Kernel trick and Mercer condition"]
[{"$nulls":4},"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"]
[{"$nulls":4},"2.5.2 SVM for nonlinear function estimation                                                  2.5.3 VC bound on generalization error                                             ; 2.6 Modifications and extensions                                       ; 2.6.1 Kernels                    ; 2.6.2 Extension to other convex cost functions                                                     ; 2.6.3 Algorithms                       ; 2.6.4 Parametric versus non-parametric approaches"]
[{"$nulls":4},"Chapter 3 Basic Methods of Least Squares Support Vector Machines"]
[null,null,null,"Sommario/riassunto","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 P"]