LEADER 04114nam 22007094a 450 001 9910809030503321 005 20200520144314.0 010 $a1-282-09618-4 010 $a0-262-25589-8 010 $a1-4294-1408-1 035 $a(CKB)1000000000466006 035 $a(EBL)3338523 035 $a(OCoLC)76824411 035 $a(SSID)ssj0000244067 035 $a(PQKBManifestationID)11237186 035 $a(PQKBTitleCode)TC0000244067 035 $a(PQKBWorkID)10164787 035 $a(PQKB)10049518 035 $a(StDuBDS)EDZ0000130718 035 $a(CaBNVSL)mat06267236 035 $a(IDAMS)0b000064818b41e0 035 $a(IEEE)6267236 035 $a(OCoLC)76824411$z(OCoLC)144221750$z(OCoLC)182530233$z(OCoLC)473855448$z(OCoLC)482338380$z(OCoLC)648224249$z(OCoLC)698448592$z(OCoLC)815786484$z(OCoLC)888487196$z(OCoLC)961552592$z(OCoLC)962681986$z(OCoLC)966247738$z(OCoLC)988479849$z(OCoLC)991907509$z(OCoLC)992079045$z(OCoLC)1011926238$z(OCoLC)1037506878$z(OCoLC)1037915633$z(OCoLC)1038619749$z(OCoLC)1055357085$z(OCoLC)1062909239$z(OCoLC)1081229069$z(OCoLC)1083554933 035 $a(OCoLC-P)76824411 035 $a(MaCbMITP)6173 035 $a(Au-PeEL)EBL3338523 035 $a(CaPaEBR)ebr10173579 035 $a(CaONFJC)MIL209618 035 $a(MiAaPQ)EBC3338523 035 $a(EXLCZ)991000000000466006 100 $a20060308d2006 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 00$aSemi-supervised learning /$f[edited by] Olivier Chapelle, Bernhard Sch?olkopf, Alexander Zien 205 $a1st ed. 210 $aCambridge, Mass. $cMIT Press$dc2006 215 $a1 online resource (528 p.) 225 1 $aAdaptive computation and machine learning 300 $aDescription based upon print version of record. 311 $a0-262-03358-5 320 $aIncludes bibliographical references (p. [479]-497). 327 $aContents; Series Foreword; Preface; 1 - Introduction to Semi-Supervised Learning; 2 - A Taxonomy for Semi-Supervised Learning Methods; 3 - Semi-Supervised Text Classification Using EM; 4 - Risks of Semi-Supervised Learning: How Unlabeled Data Can Degrade Performance of Generative Classifiers; 5 - Probabilistic Semi-Supervised Clustering with Constraints; 6 - Transductive Support Vector Machines; 7 - Semi-Supervised Learning Using Semi- Definite Programming; 8 - Gaussian Processes and the Null-Category Noise Model; 9 - Entropy Regularization; 10 - Data-Dependent Regularization 327 $a11 - Label Propagation and Quadratic Criterion12 - The Geometric Basis of Semi-Supervised Learning; 13 - Discrete Regularization; 14 - Semi-Supervised Learning with Conditional Harmonic Mixing; 15 - Graph Kernels by Spectral Transforms; 16- Spectral Methods for Dimensionality Reduction; 17 - Modifying Distances; 18 - Large-Scale Algorithms; 19 - Semi-Supervised Protein Classification Using Cluster Kernels; 20 - Prediction of Protein Function from Networks; 21 - Analysis of Benchmarks; 22 - An Augmented PAC Model for Semi- Supervised Learning 327 $a23 - Metric-Based Approaches for Semi- Supervised Regression and Classification24 - Transductive Inference and Semi-Supervised Learning; 25 - A Discussion of Semi-Supervised Learning and Transduction; References; Notation and Symbols; Contributors; Index 330 8 $aA comprehensive review of an area of machine learning that deals with the use of unlabeled data in classification problems, this text looks at state-of-the-art algorithms, applications benchmark experiments, and directions for future research. 410 0$aAdaptive computation and machine learning. 606 $aSupervised learning (Machine learning) 615 0$aSupervised learning (Machine learning) 676 $a006.3/1 701 $aChapelle$b Olivier$01720457 701 $aSch?olkopf$b Bernhard$00 701 $aZien$b Alexander$01720458 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910809030503321 996 $aSemi-supervised learning$94119128 997 $aUNINA