Prediction, Learning, and Games
This important new text and reference for researchers and students in machine learning, game theory, statistics and information theory offers the first 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...
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Prediction using expert advice provides a general framework for repeated game playing, adaptive data compression, sequential investments, sequential pattern analysis and other problems.
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.
\ From the Publisher"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\ \ \
