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Pattern Recognition and Machine Learning (Information Science and Statistics)

Author: Christopher M. Bishop
Publisher: Springer

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Languages: English (Unknown), English (Original Language), English (Published)
Media: Hardcover
Edition: 1st ed. 2006. Corr. 2nd printing
Pages: 738
Number Of Items: 1
Shipping Weight (lbs): 4.4
Dimensions (in): 9.4 x 7.2 x 1.7

ISBN: 0387310738
EAN: 9780387310732
ASIN: 0387310738

Publication Date: October 1, 2007
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Also Available In:

•	Hardcover - Pattern Recognition And Machine Learning
•	Digital - Pattern Recognition and Machine Learning (Information Science and Statistics)

Accessories:

Multimodal Technologies for Perception of Humans: First International Evaluation Workshop on Classification of Events, Activities and Relationships, ... Vision, Pattern Recognition, and Graphics)

Pixelization Paradigm: Visual Information Expert Workshop, VIEW 2006, Paris, France, April 24-25, 2006, Revised Selected Papers (Lecture Notes in ... Vision, Pattern Recognition, and Graphics)

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The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics)

Probabilistic Graphical Models: Principles and Techniques (Adaptive Computation and Machine Learning series)

Pattern Classification (2nd Edition)

Editorial Reviews:

Product Description
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.

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