000			01252nam a2200217 4500
999			_c19068 _d19068
020			_a9780387310732
082			_a006.4 BIS/P
100			_aBISHOP, CHRISTOPHER M.
245			_aPATTERN RECOGNITION AND MACHINE LEARNING
250			_a1st ed.
260			_aNew York _bSpringer _c2006
300			_a737p.
490			_aInformation science and statistics
520			_aThis 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.
650			_aComputer science
650			_aMachine learning
650			_aPattern perception
650			_aPattern recognition systems
650			_aArtificial intelligence
942			_cBK