02042nam a2200229   450000500170000000800330001702000180005004100080006808200160007610000270009224500700011926000330018930000140022244000390023650000500027552013550032565000210168065000280170165000280172965000230175765000320178020250910104516.0180202bxxu||||| |||| 00| 0 eng d  a9780387310732  aeng  a006.4bBisP  aBishop, Christopher M.  aPattern Recognition and Machine Learning /cChristopher M. Bishop  aNew York:bSpringer;c©2006  axx, 778p.  aInformation Science and Statistics  aIncludes bibliographical references and index  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.  aMachine learning  aArtificial intelligence  aMathematical statistics  aPattern perception  aPattern recognition systems