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Prediction, Learning, and Games

Prediction, Learning, and Games

$89.99 (C)

  • Date Published: March 2006
  • availability: Available
  • format: Hardback
  • isbn: 9780521841085

$ 89.99 (C)

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About the Authors
  • This important text and reference for researchers and students in machine learning, game theory, statistics and information theory offers a comprehensive treatment of the problem of predicting individual sequences. Unlike standard statistical approaches to forecasting, prediction of individual sequences does not impose any probabilistic assumption on the data-generating mechanism. Yet, prediction algorithms can be constructed that work well for all possible sequences, in the sense that their performance is always nearly as good as the best forecasting strategy in a given reference class. The central theme is the model of prediction using expert advice, a general framework within which many related problems can be cast and discussed. Repeated game playing, adaptive data compression, sequential investment in the stock market, sequential pattern analysis, and several other problems are viewed as instances of the experts' framework and analyzed from a common nonstochastic standpoint that often reveals new and intriguing connections.

    • First book to offer comprehensive treatment of the subject matter
    • Unifies different approaches developed in machine learning, game theory, statistics and information theory
    • Offers a self-contained introduction to the subject and presents the latest advances of the field
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    Reviews & endorsements

    "Each chapter contains about two dozen inspiring problems, and the book refers to more than 300 up-to-date sources.... The book is addressed to graduate students and researchers in the fields of engineering and information, computer sciences, and data analysis; it presents both theoretical inference and practical hands-on usage of modern prediction techniques."
    Stan Lipovektsy, GfK Custom Research North AmericaTechnometrics

    "This book is a comprehensive treatment of current results on predicting using expert advice."
    David S. Leslie, Mathematical Reviews

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    Product details

    • Date Published: March 2006
    • format: Hardback
    • isbn: 9780521841085
    • length: 408 pages
    • dimensions: 259 x 183 x 28 mm
    • weight: 0.86kg
    • contains: 2 tables 200 exercises
    • availability: Available
  • Table of Contents

    1. Introduction
    2. Prediction with expert advice
    3. Tight bounds for specific losses
    4. Randomized prediction
    5. Efficient forecasters for large classes of experts
    6. Prediction with limited feedback
    7. Prediction and playing games
    8. Absolute loss
    9. Logarithmic loss
    10. Sequential investment
    11. Linear pattern recognition
    12. Linear classification
    13. Appendix.

  • Authors

    Nicolo Cesa-Bianchi, Università degli Studi di Milano
    Nicolò Cesa-Bianchi is Professor of Computer Science at the University of Milan, Italy. His research interests include learning theory, pattern analysis, and worst-case analysis of algorithms. He is the acting editor of The Machine Learning Journal.

    Gabor Lugosi, Universitat Pompeu Fabra, Barcelona
    Gábor Lugosi has been working on various problems in pattern classification, nonparametric statistics, statistical learning theory, game theory, probability, and information theory. He is co-author of the monographs, A Probabilistic Theory of Pattern Recognition and Combinatorial Methods of Density Estimation. He has been an associate editor of various journals including The IEEE Transactions of Information Theory, Test, ESAIM: Probability and Statistics and Statistics and Decisions.

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