LEADER 04145nam 22006974a 450 001 9910822339903321 005 20200520144314.0 010 $a0-262-25693-2 010 $a0-585-47759-0 035 $a(CKB)111087026953862 035 $a(EBL)3338886 035 $a(OCoLC)53833203 035 $a(SSID)ssj0000190734 035 $a(PQKBManifestationID)11178475 035 $a(PQKBTitleCode)TC0000190734 035 $a(PQKBWorkID)10181031 035 $a(PQKB)11717963 035 $a(CaBNVSL)mat06267332 035 $a(IDAMS)0b000064818b42fe 035 $a(IEEE)6267332 035 $a(OCoLC)53833203$z(OCoLC)270933921$z(OCoLC)474286555$z(OCoLC)474750346$z(OCoLC)646747528$z(OCoLC)722664901$z(OCoLC)728046383$z(OCoLC)888832392$z(OCoLC)961590572$z(OCoLC)962562811$z(OCoLC)988442091$z(OCoLC)991983972$z(OCoLC)1037934695$z(OCoLC)1038570059$z(OCoLC)1055390983$z(OCoLC)1064763588$z(OCoLC)1081268807 035 $a(OCoLC-P)53833203 035 $a(MaCbMITP)4175 035 $a(Au-PeEL)EBL3338886 035 $a(CaPaEBR)ebr10229601 035 $a(MiAaPQ)EBC3338886 035 $a(EXLCZ)99111087026953862 100 $a20010907d2002 uy 0 101 0 $aeng 135 $aurcn||||||||| 181 $ctxt 182 $cc 183 $acr 200 10$aLearning with kernels $esupport vector machines, regularization, optimization, and beyond /$fBernhard Scholkopf, Alexander J. Smola 205 $a1st ed. 210 $aCambridge, Mass. $cMIT Press$dc2002 215 $a1 online resource (645 p.) 225 1 $aAdaptive computation and machine learning 300 $aDescription based upon print version of record. 311 $a0-262-19475-9 320 $aIncludes bibliographical references (p. [591]-616) and index. 327 $aContents; Series Foreword; Preface; 1 - A Tutorial Introduction; I - Concepts and Tools; 2 - Kernels; 3 - Risk and Loss Functions; 4 - Regularization; 5 - Elements of Statistical Learning Theory; 6 - Optimization; II - Support Vector Machines; 7 - Pattern Recognition; 8 - Single-Class Problems: Quantile Estimation and Novelty Detection; 9 - Regression Estimation; 10 - Implementation; 11 - Incorporating Invariances; 12 - Learning Theory Revisited; III - Kernel Methods; 13 - Designing Kernels; 14 - Kernel Feature Extraction; 15 - Kernel Fisher Discriminant; 16 - Bayesian Kernel Methods 327 $a17 - Regularized Principal Manifolds18 - Pre-Images and Reduced Set Methods; A - Addenda; B - Mathematical Prerequisites; References; Index; Notation and Symbols 330 $aIn the 1990s, a new type of learning algorithm was developed, based on results from statistical learning theory: the Support Vector Machine (SVM). This gave rise to a new class of theoretically elegant learning machines that use a central concept of SVMs -- -kernels--for a number of learning tasks. Kernel machines provide a modular framework that can be adapted to different tasks and domains by the choice of the kernel function and the base algorithm. They are replacing neural networks in a variety of fields, including engineering, information retrieval, and bioinformatics. Learning with Kernels provides an introduction to SVMs and related kernel methods. Although the book begins with the basics, it also includes the latest research. It provides all of the concepts necessary to enable a reader equipped with some basic mathematical knowledge to enter the world of machine learning using theoretically well-founded yet easy-to-use kernel algorithms and to understand and apply the powerful algorithms that have been developed over the last few years. 410 0$aAdaptive computation and machine learning. 606 $aMachine learning 606 $aAlgorithms 606 $aKernel functions 615 0$aMachine learning. 615 0$aAlgorithms. 615 0$aKernel functions. 676 $a006.3/1 700 $aScholkopf$b Bernhard$00 701 $aSmola$b Alexander J$0769130 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910822339903321 996 $aLearning with Kernels$91567641 997 $aUNINA