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  3. Prediction, Learning, and Games
  • Cited by 1382
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    • Publisher:
      Cambridge University Press
      Publication date:
      December 2009
      March 2006
      ISBN:
      9780511546921
      9780521841085
      DOI:
      https://doi.org/10.1017/CBO9780511546921
      Dimensions:
      (253 x 177 mm)
      Weight & Pages:
      0.86kg, 408 Pages
      Dimensions:
      Weight & Pages:
      • Subjects:
      Computer Science, Pattern Recognition and Machine Learning
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    • Selected: Digital
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    Subjects:
    Computer Science, Pattern Recognition and Machine Learning
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    Book description

    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.

    Reviews

    'This book is a comprehensive treatment of current results on predicting using expert advice.'

    Source: Mathematical Reviews

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