This is the first textbook on pattern recognition to present the Bayesian viewpoint. It presents approximate inference algorithms that permit fast approximate answers in situations where exact answers are not feasible, and it uses graphical models to describe probability distributions. Read More
This is the first textbook on pattern recognition to present the Bayesian viewpoint. It presents approximate inference algorithms that permit fast approximate answers in situations where exact answers are not feasible, and it uses graphical models to describe probability distributions. Read Less
Add this copy of Pattern Recognition and Machine Learning (Information to cart. $32.18, good condition, Sold by Goodwill rated 5.0 out of 5 stars, ships from Brooklyn Park, MN, UNITED STATES, published 2006 by Springer.
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Add this copy of Pattern Recognition and Machine Learning (Information to cart. $32.85, good condition, Sold by HPB-Red rated 5.0 out of 5 stars, ships from Dallas, TX, UNITED STATES, published 2006 by Springer.
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Fair. The item might be beaten up but readable. May contain markings or highlighting, as well as stains, bent corners, or any other major defect, but the text is not obscured in any way.
Add this copy of Pattern Recognition and Machine Learning (Information to cart. $45.75, good condition, Sold by BooksRun rated 4.0 out of 5 stars, ships from Philadelphia, PA, UNITED STATES, published 2006 by Springer.
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Good. It's a well-cared-for item that has seen limited use. The item may show minor signs of wear. All the text is legible, with all pages included. It may have slight markings and/or highlighting.
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.