Price: $109.99 - $69.10
(as of Oct 04, 2024 04:33:17 UTC – Details)
This is the first textbook on pattern recognition to present the Bayesian viewpoint. The book presents approximate inference algorithms that permit fast approximate answers in situations where exact answers are not feasible. It uses graphical models to describe probability distributions when no other books apply graphical models to machine learning. No previous knowledge of pattern recognition or machine learning concepts is assumed. Familiarity with multivariate calculus and basic linear algebra is required, and some experience in the use of probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.
Publisher : Springer (August 17, 2006)
Language : English
Hardcover : 738 pages
ISBN-10 : 0387310738
ISBN-13 : 978-0387310732
Item Weight : 2.31 pounds
Dimensions : 7.25 x 1.75 x 9.25 inches
Customers say
Customers find the book extremely helpful in understanding theory. They appreciate the fantastic insights and breadth of knowledge the author has accumulated into one text. However, some readers feel the math used throughout the book is quite difficult and boring. Opinions are mixed on readability, with some finding it succinct and clear, while others say it’s unreadable and annoying.
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