Pattern Recognition and Machine Learning (0387310738)

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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.

Product details

• Format Hardback | 738 pages
• Dimensions 178 x 254 x 33.02mm | 2,147g
• Publication date 06 Apr 2011
• Publisher Springer-Verlag New York Inc.
• Publication City/Country New York, NY, United States
• Language English
• Edition Statement 1st ed. 2006. Corr. 2nd printing 2011
• Illustrations note XX, 738 p.
• ISBN10 0387310738
• ISBN13 9780387310732
• Bestsellers rank 66,131

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