000			02007cam a2200289 a 4500
008			090311s2006 nyua b 001 0 eng
010			_a2006922522
020			_a9780387310732
035			_a(Sirsi) u1186
040			_aEG-CaNU _cEG-CaNU _dEG-CaNU
042			_ancode
082	0	0	_a006.4 _2 22
100	1		_aBishop, Christopher M. _9734
245	1	0	_aPattern recognition and machine learning / _c Christopher M. Bishop.
260			_aNew York : _b Springer, _c 2006.
300			_axx, 738 p. : _b ill. (some col.) ; _c 25 cm.
490	0		_aInformation science and statistics
504			_aIncludes bibliographical references and index.
505	0		_a1 Introduction -- 2 Probability Distributions -- 3 Linear Models for Regression -- 4 Linear Models for Classification -- 5 Neural Networks -- 6 Kernel Methods -- 7 Sparse Kernel Machines -- 8 Graphical Models -- 9 Mixture Models and EM -- 10 Approximate Inference -- 11 Sampling Methods -- 12 Continuous Latent Variables.
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.
630	0	0	_aCIT. _914
650		0	_aPattern perception. _9735
650		0	_aMachine learning. _9107
596			_a1
999			_c211 _d211