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Pattern Recognition And Machine Learning

ISBN: 9780387310732 | 0387310738

Format: Hardcover
Publisher: Springer-Verlag New York Inc
Pub. Date: 8/17/2006

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Summary Table of Contents Author Biography

The dramatic growth in practical applications for machine learning over the last ten years has been accompanied by many important developments in the underlying algorithms and techniques. For example, Bayesian methods have grown from a specialist niche to become mainstream, while graphical models have emerged as a general framework for describing and applying probabilistic techniques. The practical applicability of Bayesian methods has been greatly enhanced by the development of a range of approximate inference algorithms such as variational Ba... MOREyes and expectation propagation, while new models based on kernels have had a significant impact on both algorithms and applications. This completely new textbook reflects these recent developments while providing a comprehensive introduction to the fields of pattern recognition and machine learning. It is aimed at advanced undergraduates or first-year PhD students, as well as researchers and practitioners. 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. The book is suitable for courses on machine learning, statistics, computer science, signal processing, computer vision, data mining, and bioinformatics. Extensive support is provided for course instructors, including more than 400 exercises, graded according to difficulty. Example solutions for a subset of the exercises are available from the book web site, while solutions for the remainder can be obtained byinstructors from the publisher. The book is supported by a great deal of additional material, and the reader is encouraged to visit the book web site for the latest information. Coming soon: *For students, worked solutions to a subset of exercises available on a public web site (for exercises marked "www" in the text) *For instructors, worked solutions to remaining exercises from the Springer web site *Lecture slides to accompany each chapter *Data sets available for download

This is the first text on pattern recognition to present the Bayesian viewpoint, one that has become increasing popular in the last five years. It presents approximate inference algorithms that permit fast approximate answers in situations where exact answers are not feasible. It provides the first text to use graphical models to describe probability distributions when there are no other books that apply graphical models to machine learning. It is also the first four-color book on pattern recognition. The book is suitable for courses on machine learning, statistics, computer science, signal processing, computer vision, data mining, and bioinformatics. Extensive support is provided for course instructors, including more than 400 exercises, graded according to difficulty. Example solutions for a subset of the exercises are available from the book web site, while solutions for the remainder can be obtained by instructors from the publisher.

Introduction	p. 1
Probability distributions	p. 67
Linear models for regression	p. 137
Linear models for classification	p. 179
Neural networks	p. 225
Kernel methods	p. 291
Sparse kernel machines	p. 325
Graphical models	p. 359
Mixture models and EM	p. 423
Approximate inference	p. 461
Sa... MOREmpling methods	p. 523
Continuous latent variables	p. 559
Sequential data	p. 605
Combining models	p. 653
Table of Contents provided by Blackwell. All Rights Reserved.

Christopher M. Bishop is Assistant Director at Microsoft Research Cambridge, and also holds a Chair in Computer Science at the University of Edinburgh.

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