LEADER 03483nam 22006494a 450 001 9910783421103321 005 20230617003952.0 010 $a1-280-50590-7 010 $a9786610505906 010 $a1-4175-1139-7 010 $a600-00-0332-3 010 $a1-60129-401-8 035 $a(CKB)1000000000243893 035 $a(EBL)267471 035 $a(OCoLC)191037932 035 $a(SSID)ssj0000098869 035 $a(PQKBManifestationID)11113429 035 $a(PQKBTitleCode)TC0000098869 035 $a(PQKBWorkID)10136034 035 $a(PQKB)11238330 035 $a(MiAaPQ)EBC267471 035 $a(Au-PeEL)EBL267471 035 $a(CaPaEBR)ebr10116496 035 $a(CaONFJC)MIL50590 035 $a(OCoLC)70720234 035 $a(EXLCZ)991000000000243893 100 $a20030311d2003 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aAdvances in learning theory$b[electronic resource] $emethods, models, and applications /$fedited by Johan Suykens ... [et al.] 210 $aAmsterdam ;$aWashington, DC $cIOS Press ;$aTokyo $cOhmsha$dc2003 215 $a1 online resource (438 p.) 225 1 $aNATO science series. Series III, Computer and systems sciences,$x1387-6694 ;$vv. 190 300 $a"Proceedings of the NATO Advanced Study Institute on Learning Theory and Practice, 8-19 July 2002, Leuven, Belgium"--T.p. verso. 300 $a"Published in cooperation with NATO Scientific Affairs Division." 311 $a1-58603-341-7 320 $aIncludes bibliographical references and indexes. 327 $aCover; 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 327 $a9 Extension of the ?-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 330 $aThis text details advances in learning theory that relate to problems studied in neural networks, machine learning, mathematics and statistics. 410 0$aNATO science series.$nSeries III,$pComputer and systems sciences ;$vv. 190. 606 $aComputational learning theory$vCongresses 606 $aMachine learning$xMathematical models$vCongresses 615 0$aComputational learning theory 615 0$aMachine learning$xMathematical models 676 $a006.3/1 701 $aSuykens$b Johan A. K$022315 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910783421103321 996 $aAdvances in learning theory$93788314 997 $aUNINA