Prediction, Learning, and Games

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Nicolo Cesa-Bianchi, Gabor LugosiCambridge University Press, 3/13/2006EAN 9780521841085, ISBN10: 0521841089Hardcover, 408 pages, 25.9 x 18.3 x 2.8 cmLanguage: EnglishThis 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.1. Introduction2. Prediction with expert advice3. Tight bounds for specific losses4. Randomized prediction5. Efficient forecasters for large classes of experts6. Prediction with limited feedback7. Prediction and playing games8. Absolute loss9. Logarithmic loss10. Sequential investment11. Linear pattern recognition12. Linear classification13. Appendix.'This book is a comprehensive treatment of current results on predicting using expert advice.' Mathematical Reviews Show more