04114nam 22007094a 450 991080903050332120200520144314.01-282-09618-40-262-25589-81-4294-1408-1(CKB)1000000000466006(EBL)3338523(OCoLC)76824411(SSID)ssj0000244067(PQKBManifestationID)11237186(PQKBTitleCode)TC0000244067(PQKBWorkID)10164787(PQKB)10049518(StDuBDS)EDZ0000130718(CaBNVSL)mat06267236(IDAMS)0b000064818b41e0(IEEE)6267236(OCoLC)76824411(OCoLC)144221750(OCoLC)182530233(OCoLC)473855448(OCoLC)482338380(OCoLC)648224249(OCoLC)698448592(OCoLC)815786484(OCoLC)888487196(OCoLC)961552592(OCoLC)962681986(OCoLC)966247738(OCoLC)988479849(OCoLC)991907509(OCoLC)992079045(OCoLC)1011926238(OCoLC)1037506878(OCoLC)1037915633(OCoLC)1038619749(OCoLC)1055357085(OCoLC)1062909239(OCoLC)1081229069(OCoLC)1083554933(OCoLC-P)76824411(MaCbMITP)6173(Au-PeEL)EBL3338523(CaPaEBR)ebr10173579(CaONFJC)MIL209618(MiAaPQ)EBC3338523(EXLCZ)99100000000046600620060308d2006 uy 0engur|n|---|||||txtccrSemi-supervised learning /[edited by] Olivier Chapelle, Bernhard Sch?olkopf, Alexander Zien1st ed.Cambridge, Mass. MIT Pressc20061 online resource (528 p.)Adaptive computation and machine learningDescription based upon print version of record.0-262-03358-5 Includes bibliographical references (p. [479]-497).Contents; 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 Regularization11 - 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 Learning23 - 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; IndexA 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.Adaptive computation and machine learning.Supervised learning (Machine learning)Supervised learning (Machine learning)006.3/1Chapelle Olivier1720457Sch?olkopf Bernhard0Zien Alexander1720458MiAaPQMiAaPQMiAaPQBOOK9910809030503321Semi-supervised learning4119128UNINA