Pattern recognition and machine learning

Christopher M. Bishop

This is the first textbook on pattern recognition to present the Bayesian viewpoint. The book presents approximate inference algorithms that permit fast approximate answers in situations where exact answers are not feasible. It uses graphical models to describe probability distributions when no other books apply graphical models to machine learning. No previous knowledge of pattern recognition or machine learning concepts is assumed. Familiarity with multivariate calculus and basic linear algebra is required, and some experience in the use of probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.

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  • Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

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書名 Pattern recognition and machine learning
著作者等 Bishop, Christopher M
シリーズ名 Information science and statistics
出版元 Springer
刊行年月 c2006
版表示 1st ed. 2006. Corr. 2nd printing 2011
ページ数 xx, 738 p.
大きさ 25 cm
ISBN 9781493938438
NCID BA78137415
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言語 英語
出版国 アメリカ合衆国