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Advances in learning theory [[electronic resource] ] : methods, models, and applications / / edited by Johan Suykens ... [et al.]
| Advances in learning theory [[electronic resource] ] : methods, models, and applications / / edited by Johan Suykens ... [et al.] |
| Pubbl/distr/stampa | Amsterdam ; ; Washington, DC, : IOS Press |
| Descrizione fisica | 1 online resource (438 p.) |
| Disciplina | 006.3/1 |
| Altri autori (Persone) | SuykensJohan A. K |
| Collana | NATO science series. Series III, Computer and systems sciences |
| Soggetto topico |
Computational learning theory
Machine learning - Mathematical models |
| Soggetto genere / forma | Electronic books. |
| ISBN |
1-280-50590-7
9786610505906 1-4175-1139-7 600-00-0332-3 1-60129-401-8 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover; Title page; Preface; Organizing committee; List of chapter contributors; Contents; 1 An Overview of Statistical Learning Theory; 2 Best Choices for Regularization Parameters in Learning Theory: On the Bias-Variance Problem; 3 Cucker Smale Learning Theory in Besov Spaces; 4 High-dimensional Approximation by Neural Networks; 5 Functional Learning through Kernels; 6 Leave-one-out Error and Stability of Learning Algorithms with Applications; 7 Regularized Least-Squares Classification; 8 Support Vector Machines: Least Squares Approaches and Extensions
9 Extension of the ν-SVM Range for Classification10 Kernels Methods for Text Processing; 11 An Optimization Perspective on Kernel Partial Least Squares Regression; 12 Multiclass Learning with Output Codes; 13 Bayesian Regression and Classification; 14 Bayesian Field Theory: from Likelihood Fields to Hyperfields; 15 Bayesian Smoothing and Information Geometry; 16 Nonparametric Prediction; 17 Recent Advances in Statistical Learning Theory; 18 Neural Networks in Measurement Systems (an engineering view); List of participants; Subject Index; Author Index |
| Record Nr. | UNINA-9910449823103321 |
| Amsterdam ; ; Washington, DC, : IOS Press | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Advances in learning theory [[electronic resource] ] : methods, models, and applications / / edited by Johan Suykens ... [et al.]
| Advances in learning theory [[electronic resource] ] : methods, models, and applications / / edited by Johan Suykens ... [et al.] |
| Pubbl/distr/stampa | Amsterdam ; ; Washington, DC, : IOS Press |
| Descrizione fisica | 1 online resource (438 p.) |
| Disciplina | 006.3/1 |
| Altri autori (Persone) | SuykensJohan A. K |
| Collana | NATO science series. Series III, Computer and systems sciences |
| Soggetto topico |
Computational learning theory
Machine learning - Mathematical models |
| ISBN |
1-280-50590-7
9786610505906 1-4175-1139-7 600-00-0332-3 1-60129-401-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover; Title page; Preface; Organizing committee; List of chapter contributors; Contents; 1 An Overview of Statistical Learning Theory; 2 Best Choices for Regularization Parameters in Learning Theory: On the Bias-Variance Problem; 3 Cucker Smale Learning Theory in Besov Spaces; 4 High-dimensional Approximation by Neural Networks; 5 Functional Learning through Kernels; 6 Leave-one-out Error and Stability of Learning Algorithms with Applications; 7 Regularized Least-Squares Classification; 8 Support Vector Machines: Least Squares Approaches and Extensions
9 Extension of the ν-SVM Range for Classification10 Kernels Methods for Text Processing; 11 An Optimization Perspective on Kernel Partial Least Squares Regression; 12 Multiclass Learning with Output Codes; 13 Bayesian Regression and Classification; 14 Bayesian Field Theory: from Likelihood Fields to Hyperfields; 15 Bayesian Smoothing and Information Geometry; 16 Nonparametric Prediction; 17 Recent Advances in Statistical Learning Theory; 18 Neural Networks in Measurement Systems (an engineering view); List of participants; Subject Index; Author Index |
| Record Nr. | UNINA-9910783421103321 |
| Amsterdam ; ; Washington, DC, : IOS Press | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Advances in learning theory : methods, models, and applications / / edited by Johan Suykens ... [et al.]
| Advances in learning theory : methods, models, and applications / / edited by Johan Suykens ... [et al.] |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Amsterdam ; ; Washington, DC, : IOS Press |
| Descrizione fisica | 1 online resource (438 p.) |
| Disciplina | 006.3/1 |
| Altri autori (Persone) | SuykensJohan A. K |
| Collana | NATO science series. Series III, Computer and systems sciences |
| Soggetto topico |
Computational learning theory
Machine learning - Mathematical models |
| ISBN |
1-280-50590-7
9786610505906 1-4175-1139-7 600-00-0332-3 1-60129-401-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover; Title page; Preface; Organizing committee; List of chapter contributors; Contents; 1 An Overview of Statistical Learning Theory; 2 Best Choices for Regularization Parameters in Learning Theory: On the Bias-Variance Problem; 3 Cucker Smale Learning Theory in Besov Spaces; 4 High-dimensional Approximation by Neural Networks; 5 Functional Learning through Kernels; 6 Leave-one-out Error and Stability of Learning Algorithms with Applications; 7 Regularized Least-Squares Classification; 8 Support Vector Machines: Least Squares Approaches and Extensions
9 Extension of the ν-SVM Range for Classification10 Kernels Methods for Text Processing; 11 An Optimization Perspective on Kernel Partial Least Squares Regression; 12 Multiclass Learning with Output Codes; 13 Bayesian Regression and Classification; 14 Bayesian Field Theory: from Likelihood Fields to Hyperfields; 15 Bayesian Smoothing and Information Geometry; 16 Nonparametric Prediction; 17 Recent Advances in Statistical Learning Theory; 18 Neural Networks in Measurement Systems (an engineering view); List of participants; Subject Index; Author Index |
| Record Nr. | UNINA-9910814308603321 |
| Amsterdam ; ; Washington, DC, : IOS Press | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Cellular neural networks, multi-scroll chaos and synchronization / / Mustak E. Yalcin, Johan A.K. Suykens, Joos P.L. Vandewalle
| Cellular neural networks, multi-scroll chaos and synchronization / / Mustak E. Yalcin, Johan A.K. Suykens, Joos P.L. Vandewalle |
| Autore | Yalcin Mustak E |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | New Jersey ; ; London, : World Scientific, c2005 |
| Descrizione fisica | 1 online resource (247 p.) |
| Disciplina | 006.32 |
| Altri autori (Persone) |
SuykensJohan A. K
VandewalleJ <1948-> (Joos) |
| Collana | World Scientific series on nonlinear science. Series A, Monographs and treatises |
| Soggetto topico |
Neural networks (Computer science)
Nonlinear systems Chaotic behavior in systems Synchronization Computer engineering |
| ISBN |
1-281-34786-8
9786611347864 981-256-774-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Preface; Contents; Chapter 1 Introduction; Chapter 2 Cellular Neural/Nonlinear Networks; Chapter 3 Multi-Scroll Chaotic and Hyperchaotic Attractors; Chapter 4 Synchronization of Chaotic Lur'e Systems; Chapter 5 Engineering Applications; Chapter 6 General Conclusions and Future Work; Bibliography; Notation; Index |
| Record Nr. | UNINA-9910808202603321 |
Yalcin Mustak E
|
||
| New Jersey ; ; London, : World Scientific, c2005 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Cellular neural networks, multi-scroll chaos and synchronization [[electronic resource] /] / Müştak E. Yalçin, Johan A.K. Suykens, Joos P.L. Vandewalle
| Cellular neural networks, multi-scroll chaos and synchronization [[electronic resource] /] / Müştak E. Yalçin, Johan A.K. Suykens, Joos P.L. Vandewalle |
| Autore | Yalçin Müştak E |
| Pubbl/distr/stampa | New Jersey ; ; London, : World Scientific, c2005 |
| Descrizione fisica | 1 online resource (247 p.) |
| Disciplina | 006.32 |
| Altri autori (Persone) |
SuykensJohan A. K
VandewalleJ <1948-> (Joos) |
| Collana | World Scientific series on nonlinear science. Series A, Monographs and treatises |
| Soggetto topico |
Neural networks (Computer science)
Nonlinear systems Chaotic behavior in systems Synchronization Computer engineering |
| Soggetto genere / forma | Electronic books. |
| ISBN |
1-281-34786-8
9786611347864 981-256-774-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Preface; Contents; Chapter 1 Introduction; Chapter 2 Cellular Neural/Nonlinear Networks; Chapter 3 Multi-Scroll Chaotic and Hyperchaotic Attractors; Chapter 4 Synchronization of Chaotic Lur'e Systems; Chapter 5 Engineering Applications; Chapter 6 General Conclusions and Future Work; Bibliography; Notation; Index |
| Record Nr. | UNINA-9910450444903321 |
|
Yalçin Müştak E
|
||
| New Jersey ; ; London, : World Scientific, c2005 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Cellular neural networks, multi-scroll chaos and synchronization [[electronic resource] /] / Müştak E. Yalçin, Johan A.K. Suykens, Joos P.L. Vandewalle
| Cellular neural networks, multi-scroll chaos and synchronization [[electronic resource] /] / Müştak E. Yalçin, Johan A.K. Suykens, Joos P.L. Vandewalle |
| Autore | Yalçin Müştak E |
| Pubbl/distr/stampa | New Jersey ; ; London, : World Scientific, c2005 |
| Descrizione fisica | 1 online resource (247 p.) |
| Disciplina | 006.32 |
| Altri autori (Persone) |
SuykensJohan A. K
VandewalleJ <1948-> (Joos) |
| Collana | World Scientific series on nonlinear science. Series A, Monographs and treatises |
| Soggetto topico |
Neural networks (Computer science)
Nonlinear systems Chaotic behavior in systems Synchronization Computer engineering |
| ISBN |
1-281-34786-8
9786611347864 981-256-774-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Preface; Contents; Chapter 1 Introduction; Chapter 2 Cellular Neural/Nonlinear Networks; Chapter 3 Multi-Scroll Chaotic and Hyperchaotic Attractors; Chapter 4 Synchronization of Chaotic Lur'e Systems; Chapter 5 Engineering Applications; Chapter 6 General Conclusions and Future Work; Bibliography; Notation; Index |
| Record Nr. | UNINA-9910783724203321 |
|
Yalçin Müştak E
|
||
| New Jersey ; ; London, : World Scientific, c2005 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Least squares support vector machines [[electronic resource] /] / Johan A.K. Suykens ... [et al.]
| Least squares support vector machines [[electronic resource] /] / Johan A.K. Suykens ... [et al.] |
| Pubbl/distr/stampa | River Edge, NJ, : World Scientific, 2002 |
| Descrizione fisica | 1 online resource (308 p.) |
| Disciplina | 006.3/1 |
| Altri autori (Persone) | SuykensJohan A. K |
| Soggetto topico |
Machine learning
Algorithms Kernel functions Least squares |
| Soggetto genere / forma | Electronic books. |
| ISBN | 981-277-665-6 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
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 Chapter 3 Basic Methods of Least Squares Support Vector Machines |
| Record Nr. | UNINA-9910451269103321 |
| River Edge, NJ, : World Scientific, 2002 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Least squares support vector machines [[electronic resource] /] / Johan A.K. Suykens ... [et al.]
| Least squares support vector machines [[electronic resource] /] / Johan A.K. Suykens ... [et al.] |
| Pubbl/distr/stampa | River Edge, NJ, : World Scientific, 2002 |
| Descrizione fisica | 1 online resource (308 p.) |
| Disciplina | 006.3/1 |
| Altri autori (Persone) | SuykensJohan A. K |
| Soggetto topico |
Machine learning
Algorithms Kernel functions Least squares |
| ISBN | 981-277-665-6 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
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 Chapter 3 Basic Methods of Least Squares Support Vector Machines |
| Record Nr. | UNINA-9910777501403321 |
| River Edge, NJ, : World Scientific, 2002 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Least squares support vector machines / / Johan A.K. Suykens ... [et al.]
| Least squares support vector machines / / Johan A.K. Suykens ... [et al.] |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | River Edge, NJ, : World Scientific, 2002 |
| Descrizione fisica | 1 online resource (308 p.) |
| Disciplina | 006.3/1 |
| Altri autori (Persone) | SuykensJohan A. K |
| Soggetto topico |
Machine learning
Algorithms Kernel functions Least squares |
| ISBN | 981-277-665-6 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
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 Chapter 3 Basic Methods of Least Squares Support Vector Machines |
| Record Nr. | UNINA-9910813533303321 |
| River Edge, NJ, : World Scientific, 2002 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||