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Pattern recognition and machine learning / Christopher M. Bishop.

By: Material type: TextTextSeries: Information science and statisticsPublication details: New York : Springer, 2006.Description: xx, 738 p. : ill. (some col.) ; 25 cmISBN:
  • 9780387310732
Subject(s): DDC classification:
  • 006.4   22
Contents:
1 Introduction -- 2 Probability Distributions -- 3 Linear Models for Regression -- 4 Linear Models for Classification -- 5 Neural Networks -- 6 Kernel Methods -- 7 Sparse Kernel Machines -- 8 Graphical Models -- 9 Mixture Models and EM -- 10 Approximate Inference -- 11 Sampling Methods -- 12 Continuous Latent Variables.
Summary: 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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Holdings
Item type Current library Call number Copy number Status Date due Barcode
Books Books Main library General Stacks 006.4 / BI.P 2006 (Browse shelf(Opens below)) 1 Available 002289
Books Books Main library General Stacks 006.4 / BI.P 2006 (Browse shelf(Opens below)) 2 Available 002288

Includes bibliographical references and index.

1 Introduction -- 2 Probability Distributions -- 3 Linear Models for Regression -- 4 Linear Models for Classification -- 5 Neural Networks -- 6 Kernel Methods -- 7 Sparse Kernel Machines -- 8 Graphical Models -- 9 Mixture Models and EM -- 10 Approximate Inference -- 11 Sampling Methods -- 12 Continuous Latent Variables.

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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