Advances in learning theory : methods, models, and applications / / edited by Johan Suykens ... [et al.]

• Record Nr.: UNINA9910814308603321
• Pubbl/distr/stampa: Amsterdam ; ; Washington, DC, : IOS Press; Tokyo, : Ohmsha, c2003
• ISBN: 1-280-50590-7; 9786610505906; 1-4175-1139-7; 600-00-0332-3; 1-60129-401-8
• Edizione: [1st ed.]
• Descrizione fisica: 1 online resource (438 p.)
• Collana: NATO science series. Series III, Computer and systems sciences, , 1387-6694 ; ; v. 190
• Altri autori (Persone): SuykensJohan A. K
• Disciplina: 006.3/1
• Soggetti: Computational learning theory; Machine learning - Mathematical models
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Note generali: "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."
• Nota di bibliografia: Includes bibliographical references and indexes.
• 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
• Sommario/riassunto: This text details advances in learning theory that relate to problems studied in neural networks, machine learning, mathematics and statistics.