Prediction, learning, and games

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Title:

Author:

Cesa-Bianchi, Nicolò, 1963-

ISBN:

9780521841085

Personal Author:

Cesa-Bianchi, Nicolò, 1963-

Publication Information:

Cambridge : Cambridge University Press, 2006.

Physical Description:

xii, 394 p. : ill. ; 27 cm.

General Note:

Formerly CIP.

Contents:

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.

Abstract:

"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."--BOOK JACKET.

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Library	Material Type	Item Barcode	Shelf Number	Copy	Status
Searching... 1:IIEMSA	1:GEN-BOOK	33168025461464	519.3 C421P 2006	1	Searching... Unknown

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Summary

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.