02510nam 22005654a 450 991066679550332120251217201743.00-262-26107-397866120979661-4237-6990-2(CKB)1000000000461032(CtWfDGI)bks00005224(Au-PeEL)EBL3338604(CaPaEBR)ebr10173663(CaONFJC)MIL209796(OCoLC)68194203(FR-PaCSA)88800169(MiAaPQ)EBC3338604(EXLCZ)99100000000046103220050812d2006 uy 0engurzn||||||txtrdacontentcrdamediacrrdacarrierGaussian processes for machine learning /Carl Edward Rasmussen, Christopher K.I. Williams1st ed.Cambridge, Mass. MIT Pressc2006xviii, 248 p. illAdaptive computation and machine learningTitle from title screen.0-262-18253-X Includes bibliographical references (p. [223]-238) and indexes.Intro -- Series Foreword -- Preface -- Symbols and Notation -- Chapter 1 Introduction -- Chapter 2 Regression -- Chapter 3 Classification -- Chapter 4 Covariance functions -- Chapter 5 Model Selection and Adaptation of Hyperparameters -- Chapter 6 Relationships between GPs and Other Models -- Chapter 7 Theoretical Perspectives -- Chapter 8 Approximation Methods for Large Datasets -- Chapter 9 Further Issues and Conclusions -- Appendix A Mathematical Background -- Appendix B Gaussian Markov Processes -- Appendix C Datasets and Code -- Bibliography -- Author Index -- Subject Index.A comprehensive and self-contained introduction to Gaussian processes, which provide a principled, practical, probabilistic approach to learning in kernel machines.Adaptive computation and machine learning.Gaussian processesData processingMachine learningMathematical modelsGaussian processesData processing.Machine learningMathematical models.519.2/3Rasmussen Carl Edward1135484Williams Christopher K. I1135485MiAaPQMiAaPQMiAaPQBOOK9910666795503321Gaussian processes for machine learning2670841UNINA