New York, NY Springer 2006 2006. Corr. 2nd Printing 2011 ed. Hard cover New. Sewn binding. Paper over boards. 778 p. Information Science and Statistics.

ISBN10: 0387310738, ISBN13: 9780387310732, [publisher: Springer-Verlag New York Inc.] Hardcover 100% Customer Satisfaction Guaranteed ! The book shows some signs of wear from use but is a good readable copy. Cover in excellent condition. Binding tight. Pages in great shape, no tears. Not contain access codes, cd, DVD.
[Suffolk, United Kingdom] [Publication Year: 2006]
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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.
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ISBN10: 0387310738, ISBN13: 9780387310732, [publisher: Springer] Hardcover
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ISBN10: 0387310738, ISBN13: 9780387310732, [publisher: Springer] Hardcover
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ISBN10: 0387310738, ISBN13: 9780387310732, [publisher: Springer] Hardcover Very Good Copy. Customer Service Guaranteed
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ISBN10: 0387310738, ISBN13: 9780387310732, [publisher: Springer] Hardcover
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ISBN10: 0387310738, ISBN13: 9780387310732, [publisher: Springer] Hardcover SHIPS FROM USA. Used books have different signs of use and do not include supplemental materials such as CDs, Dvds, Access Codes, charts or any other extra material. All used books might have various degrees of writing, highliting and wear and tear and possibly be an ex-library with the usual stickers and stamps. Dust Jackets are not guaranteed and when still present, they will have various degrees of tear and damage. All images are Stock Photos, not of the actual item.
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ISBN10: 0387310738, ISBN13: 9780387310732, [publisher: Springer] Hardcover May have some shelf-wear due to normal use. Your purchase funds free job training and education in the greater Seattle area. Thank you for supporting Goodwill's nonprofit mission!
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ISBN10: 0387310738, ISBN13: 9780387310732, [publisher: Springer] Hardcover Ships in a BOX from Central Missouri! May not include working access code. Will not include dust jacket. Has used sticker(s) and some writing or highlighting. UPS shipping for most packages, (Priority Mail for AK/HI/APO/PO Boxes).
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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 (edition )] Hardcover Ship within 24hrs. Satisfaction 100% guaranteed. APO/FPO addresses supported
[Philadelphia, PA, U.S.A.] [Publication Year: 2006]

ISBN10: 0387310738, ISBN13: 9780387310732, [publisher: Springer, India] Softcover No markings.
[Menlo Park, CA, U.S.A.] [Publication Year: 2013]

SPRINGER. Used - Good. May have notes, highlighting, wear and tear. Supplements not guaranteed. Please contact us if you have any questions. 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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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-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-Verlag New York Inc 2006 2006. Corr. 2nd Printing 2011 ed. Hard cover 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.

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] 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 ...

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