Gaussian processes for machine learning / / Carl Edward Rasmussen, Christopher K.I. Williams.

A comprehensive and self-contained introduction to Gaussian processes, which provide a principled, practical, probabilistic approach to learning in kernel machines.

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Bibliographic Details
Superior document:Adaptive computation and machine learning
:
TeilnehmendeR:
Year of Publication:2006
Edition:1st ed.
Language:English
Series:Adaptive computation and machine learning.
Physical Description:xviii, 248 p. :; ill.
Notes:Title from title screen.
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245 1 0 |a Gaussian processes for machine learning /  |c Carl Edward Rasmussen, Christopher K.I. Williams. 
250 |a 1st ed. 
256 |a Computer document. 
260 |a Cambridge, Mass. :  |b MIT Press,  |c cop. 2006. 
300 |a xviii, 248 p. :  |b ill. 
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490 1 |a Adaptive computation and machine learning 
500 |a Title from title screen. 
516 |a Text. 
550 |a Digitized and made available by: Books24x7.com. 
530 |a Available also in a print ed. 
504 |a Includes bibliographical references (p. [223]-238) and indexes. 
520 |a A comprehensive and self-contained introduction to Gaussian processes, which provide a principled, practical, probabilistic approach to learning in kernel machines. 
505 0 |a 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. 
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650 0 |a Gaussian processes  |x Data processing. 
650 0 |a Machine learning  |x Mathematical models. 
700 1 |a Williams, Christopher K. I. 
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