ISBN10: 0387310738, ISBN13: 9780387310732, [publisher: Springer] Hardcover Good condition. This is the average used book, that has all pages or leaves present, but may include writing. Book may be ex-library with stamps and stickers.
[Tucson, AZ, U.S.A.] [Publication Year: 2006]

ISBN10: 0387310738, ISBN13: 9780387310732, [publisher: Springer New York 2006-08-17, Berlin] Hardcover Language: ENG
[London, United Kingdom] [Publication Year: 2006]

ISBN10: 0387310738, ISBN13: 9780387310732, [publisher: Springer-Verlag New York Inc.] Hardcover This is an ex-library book and may have the usual library/used-book markings inside.This book has hardback covers. In good all round condition. No dust jacket. Please note the Image in this listing is a stock photo and may not match the covers of the actual item,1850grams, ISBN:9780387310732
[Lincoln, United Kingdom] [Publication Year: 2006]

Springer-Verlag New York Inc., Date: 2006. This is an ex-library book and may have the usual library/used-book markings inside.This book has hardback covers. In good all round condition. No dust jacket. Please note the Image in this listing is a stock photo and may not match the covers of the actual item,1850grams, ISBN:9780387310732 2006. Springer-Verlag New York Inc. ISBN 0387310738 9780387310732 [GB]

Springer, Date: 2006-08-17. Hardcover. Like New. Clean, sturdy, unmarked copy in like new condition. Great copy! Looks like new. SZ 2006. Springer ISBN 0387310738 9780387310732 [US]

ISBN10: 0387310738, ISBN13: 9780387310732, [publisher: Springer Nature Singapore Aug 2006] Hardcover Neuware - 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.
[Einbeck, Germany] ...
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