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 ...
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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.
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Add this copy of Pattern Recognition and Machine Learning to cart. $73.17, good condition, Sold by GreatBookPricesUK5 rated 5.0 out of 5 stars, ships from Castle Donington, DERBYSHIRE, UNITED KINGDOM, published 2006 by Springer.
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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.
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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.
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Fast shipping, good brand new book and good print. Price is reasonable.
Dxxxx
Oct 30, 2008
As good as it gets
This book makes for terrible machine learning study material -- it's entirely equations, with no intuitive explanations or even real-life examples. It might be a good reference for someone who already has an intuitive understanding of the algorithms. Unfortunately, I've done some research and haven't found any books that are better overall. Tom Mitchell's book has a far better explanation of traditional machine learning methods, but it doesn't cover SVMs, which should be the meat of any modern machine learning course. I'd tell a friend to buy Mitchell's book instead, then using various papers, the site svms.org, and the tutorial by Burges to learn SVMs.
