Pattern Recognition and Machine Learning / Edition 1

Pattern Recognition and Machine Learning / Edition 1

by Christopher Bishop
     
 

ISBN-10: 0387310738

ISBN-13: 9780387310732

Pub. Date: 04/06/2011

Publisher: Springer New York

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

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Overview

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.

Product Details

ISBN-13:
9780387310732
Publisher:
Springer New York
Publication date:
04/06/2011
Series:
Information Science and Statistics Series
Edition description:
1st ed. 2006. Corr. 2nd printing 2011
Pages:
738
Sales rank:
283,188
Product dimensions:
7.01(w) x 10.00(h) x 0.07(d)

Related Subjects

Table of Contents

Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

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