Pattern Recognition and Machine Learning

Pattern Recognition and Machine Learning - Information Science and Statistics

1st ed. 2006. Corr. 2nd printing 2011

Hardback (17 Aug 2006)

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Synopsis

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.

Book information

ISBN: 9780387310732
Publisher: Springer New York
Imprint: Springer
Pub date:
Edition: 1st ed. 2006. Corr. 2nd printing 2011
DEWEY: 006.4
DEWEY edition: 23
Language: English
Number of pages: xx, 738
Weight: 1892g
Height: 242mm
Width: 188mm
Spine width: 42mm