Pattern Recognition and Machine Learning (Information...

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Pattern Recognition and Machine...

Pattern Recognition and Machine Learning (Information Science and Statistics)

Christopher M. Bishop

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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.

카테고리:

Computers - Computer Science

년:

2006

판:

1st ed. 2006. Corr. 2nd printing

출판사:

Springer Science+Business Media, LLC

언어:

english

페이지:

761