Prediction, Learning, and Games

TRAMA

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 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. Old and new forecasting methods are described in a mathematically precise way in order to characterize their theoretical limitations and possibilities.

NOTE EDITORE

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.

SOMMARIO

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.

PREFAZIONE

The central theme here is a model of prediction using expert advice, a general framework within which many related problems can be cast and discussed, including repeated game playing, adaptive data compression, sequential investment in the stock market, and sequential pattern analysis.

AUTORE

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.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.

ALTRE INFORMAZIONI

• Condizione: Nuovo
• ISBN: 9780521841085
• Dimensioni: 259 x 28 x 183 mm Ø 860 gr
• Formato: Copertina rigida
• Illustration Notes: 2 tables 200 exercises
• Pagine Arabe: 408