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[null,"1.","Record Nr.",null,"UNINA9910449823103321"]
[null,null,"Titolo",null,"Advances in learning theory [[electronic resource] ] : methods, models, and applications / / edited by Johan Suykens ... [et al.]"]
[null,null,"Pubbl/distr/stampa",null,"Amsterdam ; ; Washington, DC, : IOS Press"]
[{"$nulls":4},"Tokyo, : Ohmsha, c2003"]
[null,null,"ISBN",null,"1-280-50590-7"]
[{"$nulls":4},"9786610505906"]
[{"$nulls":4},"1-4175-1139-7"]
[{"$nulls":4},"600-00-0332-3"]
[{"$nulls":4},"1-60129-401-8"]
[null,null,"Descrizione fisica",null,"1 online resource (438 p.)"]
[null,null,"Collana",null,"NATO science series. Series III, Computer and systems sciences, , 1387-6694 ; ; v. 190"]
[null,null,"Disciplina",null,"006.3/1"]
[null,null,"Soggetti",null,"Computational learning theory"]
[{"$nulls":4},"Machine learning - Mathematical models"]
[{"$nulls":4},"Electronic books."]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","\"Proceedings of the NATO Advanced Study Institute on Learning Theory and Practice, 8-19 July 2002, Leuven, Belgium\"--T.p. verso."]
[{"$nulls":4},"\"Published in cooperation with NATO Scientific Affairs Division.\""]
[null,null,null,"Nota di bibliografia","Includes bibliographical references and indexes."]
[null,null,"Nota di contenuto",null,"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"]
[{"$nulls":4},"9 Extension of the ν-SVM Range for Classification10 Kernels Methods for Text Processing; 11 An Optimization Perspective on Kernel Partial Least Squares Regression; 12 Multiclass Learning with Output Codes; 13 Bayesian Regression and Classification; 14 Bayesian Field Theory: from Likelihood Fields to Hyperfields; 15 Bayesian Smoothing and Information Geometry; 16 Nonparametric Prediction; 17 Recent Advances in Statistical Learning Theory; 18 Neural Networks in "]
[{"$nulls":4},"Measurement Systems (an engineering view); List of participants; Subject Index; Author Index"]
[null,null,null,"Sommario/riassunto","This text details advances in learning theory that relate to problems studied in neural networks, machine learning, mathematics and statistics."]