Pattern Recognition and Machine Learning

Pattern Recognition and Machine Learning - Information Science and Statistics

1st ed. 2006. Corr. 2nd printing 2011

Hardback (17 Aug 2006)

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Publisher's Synopsis

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.

Book information

ISBN: 9780387310732
Publisher: Springer New York
Imprint: Springer
Pub date:
Edition: 1st ed. 2006. Corr. 2nd printing 2011
DEWEY: 006.4
DEWEY edition: 23
Language: English
Number of pages: xx, 738
Weight: 1510g
Height: 242mm
Width: 188mm
Spine width: 42mm