Pattern Recognition and Machine Learning_Christopher M. Bishop.pdf

Information Science and Statistics Series Editors: M. Jordan J. Kleinberg B. Scho¨lkopf Information Science and Statistics Akaike and Kitagawa: The Practice of Time Series Analysis. Bishop: Pattern Recognition and Machine Learning. Cowell, Dawid, Lauritzen, and Spiegelhalter: Probabilistic Networks and Expert Systems. Doucet, de Freitas, and Gordon: Sequential Monte Carlo Methods in Practice. Fine: Feedforward Neural Network Methodology. Hawkins and Olwell: Cumulative Sum Charts and Charting for Quality Improvement. Jensen:Bayesian Networks and Decision Graphs. Marchette: Computer Intrusion Detection and Network Monitoring: A Statistical Viewpoint. Rubinstein and Kroese:The Cross-Entropy Method: A Unified Approach to Combinatorial Optimization, Monte Carlo Simulation, and Machine Learning. Studený: Probabilistic Conditional Independence Structures. Vapnik: The Nature of Statistical Learning Theory, Second Edition. Wallace: Statistical and Inductive Inference by Minimum Massage Length. Christopher M. Bishop PatternRecognitionand MachineLearning Christopher M. Bishop F.R.Eng. Assistant Director Microsoft Research Ltd Cambridge CB3 0FB, U.K. [email protected] http://research.microsoft.com/©cmbishop Series Editors Michael Jordan Department of Computer Science and Department of Statistics University of California, Berkeley Berkeley, CA 94720 USA Professor Jon Kleinberg Department of Computer Science Cornell University Ithaca, NY 14853 USA Bernhard Scho¨lkopf Max Planck Institute for Biological Cybernetics Spemannstrasse 38 72076 Tu¨bingen Germany Library of Congress Control Number: 2006922522 ISBN-10: 0-387-31073-8 ISBN-13: 978-0387-31073-2 Printed on acid-free paper. ©2006 Springer Science+Business Media, LLC All rights reserved. This work may not be translated or copied in whole or in part without the written permission of the publisher (Springer Science+Business Media, LLC, 233 Spring Street, New York, NY 10013, USA), except for brief excerpts in