03480nam 22006614a 450 991081430860332120200520144314.01-280-50590-797866105059061-4175-1139-7600-00-0332-31-60129-401-8(CKB)1000000000243893(EBL)267471(OCoLC)191037932(SSID)ssj0000098869(PQKBManifestationID)11113429(PQKBTitleCode)TC0000098869(PQKBWorkID)10136034(PQKB)11238330(MiAaPQ)EBC267471(Au-PeEL)EBL267471(CaPaEBR)ebr10116496(CaONFJC)MIL50590(OCoLC)70720234(EXLCZ)99100000000024389320030311d2003 uy 0engur|n|---|||||txtccrAdvances in learning theory methods, models, and applications /edited by Johan Suykens ... [et al.]1st ed.Amsterdam ;Washington, DC IOS Press ;Tokyo Ohmshac20031 online resource (438 p.)NATO science series. Series III, Computer and systems sciences,1387-6694 ;v. 190"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."1-58603-341-7 Includes bibliographical references and indexes.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 Extensions9 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 IndexThis text details advances in learning theory that relate to problems studied in neural networks, machine learning, mathematics and statistics.NATO science series.Series III,Computer and systems sciences ;v. 190.Computational learning theoryCongressesMachine learningMathematical modelsCongressesComputational learning theoryMachine learningMathematical models006.3/1Suykens Johan A. K22315MiAaPQMiAaPQMiAaPQBOOK9910814308603321Advances in learning theory4059527UNINA