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Record Nr. |
UNINA9910814308603321 |
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Titolo |
Advances in learning theory : methods, models, and applications / / edited by Johan Suykens ... [et al.] |
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Pubbl/distr/stampa |
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Amsterdam ; ; Washington, DC, : IOS Press |
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Tokyo, : Ohmsha, c2003 |
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ISBN |
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1-280-50590-7 |
9786610505906 |
1-4175-1139-7 |
600-00-0332-3 |
1-60129-401-8 |
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Edizione |
[1st ed.] |
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Descrizione fisica |
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1 online resource (438 p.) |
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Collana |
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NATO science series. Series III, Computer and systems sciences, , 1387-6694 ; ; v. 190 |
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Altri autori (Persone) |
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Disciplina |
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Soggetti |
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Computational learning theory |
Machine learning - Mathematical models |
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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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"Proceedings of the NATO Advanced Study Institute on Learning Theory and Practice, 8-19 July 2002, Leuven, Belgium"--T.p. verso. |
"Published in cooperation with NATO Scientific Affairs Division." |
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Nota di bibliografia |
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Includes bibliographical references and indexes. |
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Nota di contenuto |
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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 |
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