Prediction, Learning, and Games

This book provides a general framework for repeated game playing, adaptive data compression, sequential investments, sequential pattern analysis and other problems.

Nicolo Cesa-Bianchi (Author), Gabor Lugosi (Author)

9780521841085, Cambridge University Press

Hardback, published 13 March 2006

408 pages, 2 tables 200 exercises
25.9 x 18.3 x 2.8 cm, 0.86 kg

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

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.

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.

Subject Areas: Pattern recognition [UYQP], Game theory [PBUD], Probability & statistics [PBT]