ISBN10: 0387310738, ISBN13: 9780387310732, [publisher: Springer] Hardcover
[Moncks Corner, SC, U.S.A.] [Publication Year: 2006]

ISBN10: 0387310738, ISBN13: 9780387310732, [publisher: Springer] Hardcover Buy for Great customer experience
[Houston, TX, U.S.A.] [Publication Year: 2006]

ISBN10: 0387310738, ISBN13: 9780387310732, [publisher: Springer] Hardcover
[Denver, CO, U.S.A.] [Publication Year: 2006]

Springer 2006-08-17 2006. Corr. 2nd Printing 2011 ed. Hardcover New Size: 10x7x1; New. In shrink wrap. Looks like an interesting title!

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] Hardcover Prompt service guaranteed.
[Tijeras, NM, U.S.A.] [Publication Year: 2006]

ISBN10: 0387310738, ISBN13: 9780387310732, [publisher: Springer] Hardcover
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ISBN10: 0387310738, ISBN13: 9780387310732, [publisher: Springer] Hardcover Very Good Copy. Customer Service Guaranteed
[Denver, CO, U.S.A.] [Publication Year: 2006]

Berlin Springer New York 2006 2006. Corr. 2nd Printing 2011 ed. Hard cover New in new dust jacket.

ISBN10: 0387310738, ISBN13: 9780387310732, [publisher: Springer] Hardcover New! This book is in the same immaculate condition as when it was published
[Tucson, AZ, U.S.A.] [Publication Year: 2006]

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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Springer 2006-08-17 2006. Corr. 2nd Printing 2011 ed. Hardcover Good US Edition Textbook, May Have Highlights, Notes and/or Underlining, BOOK ONLY-NO ACCESS CODE, NO CD, Ships with Emailed Tracking from USA.

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. 778 pp. Englisch ...
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Springer, Date: 2006-08-17. hardcover. Used:Good. 2006. Springer ISBN 0387310738 9780387310732 [US]

Springer 2006 2006. Corr. 2nd Printing 2011 ed. Hard cover New

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

Springer 2006-08-17 2006. Corr. 2nd Printing 2011 ed. Hardcover Good USED-VARIOUS AMOUNTS OF WEAR-POSSIBLE HIGHLIGHTS-WE CANNOT GUARANTEE SUPPLEMENTS SUCH AS CD, ACCESS CODE, OR INFO TRAC.

ISBN10: 0387310738, ISBN13: 9780387310732, [publisher: Springer-Verlag New York Inc.] Hardcover Book is in NEW condition. Satisfaction Guaranteed! Fast Customer Service!!
[Suffolk, United Kingdom] [Publication Year: 2006]
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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]

ISBN10: 0387310738, ISBN13: 9780387310732, [publisher: Springer New York] Hardcover First text on pattern recognition to present the Bayesian viewpoint, one that has become increasing popular in the last five years. Presents approximate inference algorithms that permit fast approximate answers in situations where exact answers ar.
[Greven, Germany] [Publication Year: 2006]
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New. Well packaged and promptly shipped from California. Partnered with Friends of the Library since 2010. ISBN 0387310738 9780387310732 [US]

ISBN10: 0387310738, ISBN13: 9780387310732, [publisher: Springer] Hardcover Well packaged and promptly shipped from California. Partnered with Friends of the Library since 2010.
[San Rafael, CA, U.S.A.] [Publication Year: 2006]

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-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 ...

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]