Springer. Very Good. Date: 2006. Hardcover. 0387310738 . This book is in very good condition; no remainder marks. It does have some cover shelfwear and corner wear. Inside pages are clean. ; Information Science and Statistics; Height: 10.2 Inches, Leng; 738 pages . 2006. Springer ISBN 0387310738 9780387310732 [US]

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 New York 2006-08-17, Berlin] Hardcover Language: ENG
[London, United Kingdom] [Publication Year: 2006]

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

Springer, August Date: 2006. Hardcover . Like New/no No Jacket. As new, tight and square, no writing, sharp 2006. Springer ISBN 0387310738 9780387310732 [US]

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.

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 2009 2006. Corr. 2nd Printing 2011 ed. Hardcover 2009. Springer. Hardcover. GOOD Blue and yellow titles, illustrated boards. 10x7.

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]

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]

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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This book is in very good condition and ready for quick shipment ISBN 0387310738 9780387310732 [US]

Springer 2006 2006. Corr. 2nd Printing 2011 ed. Hard cover Good May have notes, highlighting, wear and tear. Supplements not guaranteed. Please contact us if you have any questions.

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[Philadelphia, PA, U.S.A.] [Publication Year: 2006]

ISBN10: 0387310738, ISBN13: 9780387310732, [publisher: Springer (edition )] Hardcover Ship within 24hrs. Satisfaction 100% guaranteed. APO/FPO addresses supported
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ISBN10: 0387310738, ISBN13: 9780387310732, [publisher: Springer] Hardcover New
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ISBN10: 0387310738, ISBN13: 9780387310732, [publisher: Springer] Hardcover Noticeable lower/upper bind wear. Text is mostly clean readable.
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ISBN10: 0387310738, ISBN13: 9780387310732, [publisher: Springer] Hardcover
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ISBN10: 0387310738, ISBN13: 9780387310732, [publisher: Springer] Hardcover Minimal wear to cover. Pages clean and binding tight. Shelfwear. Bumped edges. Minor Exterior Wear. Pages clean. Binding tight. Hardcover.
[New York, NY, U.S.A.] [Publication Year: 2006]

ISBN10: 0387310738, ISBN13: 9780387310732, [publisher: Springer] Hardcover
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ISBN10: 0387310738, ISBN13: 9780387310732, [publisher: Springer] Hardcover Buy for Great customer experience
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ISBN10: 0387310738, ISBN13: 9780387310732, [publisher: Springer] Hardcover New Copy. Customer Service Guaranteed
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ISBN10: 0387310738, ISBN13: 9780387310732, [publisher: Springer] Hardcover
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