LEADER 04096nam 22007212 450 001 9910457168903321 005 20151005020622.0 010 $a1-107-14456-6 010 $a1-139-63694-4 010 $a1-280-51598-8 010 $a9786610515981 010 $a0-511-21418-9 010 $a0-511-21597-5 010 $a0-511-21060-4 010 $a0-511-31495-7 010 $a0-511-80968-9 010 $a0-511-21237-2 035 $a(CKB)1000000000353759 035 $a(EBL)266541 035 $a(OCoLC)144618454 035 $a(SSID)ssj0000187106 035 $a(PQKBManifestationID)11197239 035 $a(PQKBTitleCode)TC0000187106 035 $a(PQKBWorkID)10252910 035 $a(PQKB)10933246 035 $a(UkCbUP)CR9780511809682 035 $a(MiAaPQ)EBC266541 035 $a(Au-PeEL)EBL266541 035 $a(CaPaEBR)ebr10131674 035 $a(CaONFJC)MIL51598 035 $a(EXLCZ)991000000000353759 100 $a20101021d2004|||| uy| 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aKernel methods for pattern analysis /$fJohn Shawe-Taylor, Nello Cristianini$b[electronic resource] 210 1$aCambridge :$cCambridge University Press,$d2004. 215 $a1 online resource (xiv, 462 pages) $cdigital, PDF file(s) 300 $aTitle from publisher's bibliographic system (viewed on 05 Oct 2015). 311 $a0-521-81397-2 320 $aIncludes bibliographical references (p. 450-459) and index. 327 $aCover; Half-title; Title; Copyright; Contents; Code fragments; Preface; 1 Pattern analysis; 2 Kernel methods: an overview; 3 Properties of kernels; 4 Detecting stable patterns; 5 Elementary algorithms in feature space; 6 Pattern analysis using eigen-decompositions; 7 Pattern analysis using convex optimisation; 8 Ranking, clustering and data visualisation; 9 Basic kernels and kernel types; 10 Kernels for text; 11 Kernels for structured data: strings, trees, etc.; 12 Kernels from generative models; Appendix A Proofs omitted from the main text; A.1 Proof of McDiarmid's theorem 327 $aA.2 Stability of principal components analysisA.3 Proofs of diffusion kernels; Appendix B Notational conventions; B.1 List of symbols; B.2 Notation for Tables; Appendix C List of pattern analysis methods; C.1 Pattern analysis computations; C.2 Pattern analysis algorithms; Appendix D List of kernels; D.1 Kernel definitions and computations; D.2 Kernel algorithms; References; Index 330 $aKernel methods provide a powerful and unified framework for pattern discovery, motivating algorithms that can act on general types of data (e.g. strings, vectors or text) and look for general types of relations (e.g. rankings, classifications, regressions, clusters). The application areas range from neural networks and pattern recognition to machine learning and data mining. This book, developed from lectures and tutorials, fulfils two major roles: firstly it provides practitioners with a large toolkit of algorithms, kernels and solutions ready to use for standard pattern discovery problems in fields such as bioinformatics, text analysis, image analysis. Secondly it provides an easy introduction for students and researchers to the growing field of kernel-based pattern analysis, demonstrating with examples how to handcraft an algorithm or a kernel for a new specific application, and covering all the necessary conceptual and mathematical tools to do so. 606 $aMachine learning 606 $aAlgorithms 606 $aKernel functions 606 $aPattern perception$xData processing 615 0$aMachine learning. 615 0$aAlgorithms. 615 0$aKernel functions. 615 0$aPattern perception$xData processing. 676 $a006.3/1 700 $aShawe-Taylor$b John$0760509 702 $aCristianini$b Nello 801 0$bUkCbUP 801 1$bUkCbUP 906 $aBOOK 912 $a9910457168903321 996 $aKernel methods for pattern analysis$92465958 997 $aUNINA