Pattern Recognition and Machine Learning (Hardcover, 1st ed. 2006. Corr. 2nd printing 2011)

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.

General

Imprint:	Springer-Verlag New York
Country of origin:	United States
Series:	Information Science and Statistics
Release date:	April 2011
First published:	2006
Authors:	Christopher M. Bishop
Dimensions:	242 x 179 x 40mm (L x W x T)
Format:	Hardcover
Pages:	738
Edition:	1st ed. 2006. Corr. 2nd printing 2011
ISBN-13:	978-0-387-31073-2
Categories:	Books > Computing & IT > Applications of computing > Artificial intelligence > Computer vision
LSN:	0-387-31073-8
Barcode:	9780387310732

Is the information for this product incomplete, wrong or inappropriate? Let us know about it.

Does this product have an incorrect or missing image? Send us a new image.

Is this product missing categories? Add more categories.

Review This Product

No reviews yet - be the first to create one!