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020			_a9780387310732
041			_aeng
082			_a006.4 _bBisP
100			_aBishop, Christopher M.
245			_aPattern Recognition and Machine Learning / _cChristopher M. Bishop
260			_aNew York: _bSpringer; _c©2006
300			_axx, 778p.
440			_aInformation Science and Statistics
500			_aIncludes bibliographical references and index
520			_aPattern recognition has its origins in engineering, whereas machine learning grew out of computer science. However, these activities can be viewed as two facets of the same field, and together they have undergone substantial development over the past ten years. In particular, Bayesian methods have grown from a specialist niche to become mainstream, while graphical models have emerged as a general framework for describing and applying probabilistic models. Also, the practical applicability of Bayesian methods has been greatly enhanced through the development of a range of approximate inference algorithms such as variational Bayes and expectation pro- gation. Similarly, new models based on kernels have had significant impact on both algorithms and applications. This new textbook reacts these recent developments while providing a comprehensive introduction to the fields of pattern recognition and machine learning. It is aimed at advanced undergraduates or first year PhD students, as wellas researchers and practitioners, and assumes no previous knowledge of pattern recognition or - chine learning concepts. Knowledge of multivariate calculus and basic linear algebra is required, and some familiarity with probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.
650			_aMachine learning
650			_aArtificial intelligence
650			_aMathematical statistics
650			_aPattern perception
650			_aPattern recognition systems
942			_cBK
999			_c1337 _d1337