ISBN10: 0387310738, ISBN13: 9780387310732, [publisher: Springer] Hardcover 2009. Springer. Hardcover. GOOD Blue and yellow titles, illustrated boards. 10x7
[Cambridge, GLOUC, United Kingdom] [Publication Year: 2009]

ISBN10: 0387310738, ISBN13: 9780387310732, [publisher: Springer August 2006] Hardcover As new, tight and square, no writing, sharp
[Pella, IA, U.S.A.] [Publication Year: 2006]

Springer Verlag 2006 2006. Corr. 2nd Printing 2011 ed. Hardcover New 1st edition. 738 pages. 9.25x7.25x1.75 inches.

Springer Verlag, Date: 2006. Hardcover. New. 1st edition. 738 pages. 9.25x7.25x1.75 inches. 2006. Springer Verlag ISBN 0387310738 9780387310732 [GB]

ISBN10: 0387310738, ISBN13: 9780387310732, [publisher: Springer-Verlag New York Inc., United States, New York, NY] Softcover Pattern 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. The book has ...