New York, NY Springer 2006 2006. Corr. 2nd Printing 2011 ed. Hard cover Good. Sewn binding. Paper over boards. 778 p. Information Science and Statistics. May show signs of wear, highlighting, writing, and previous use. This item may be a former library book with typical markings. No guarantee on products that contain supplements Your satisfaction is 100% guaranteed. Twenty-five year bookseller with shipments to over fifty million happy customers.

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

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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