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 seque...

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Bibliographic Details

Main Authors:	Cesa-Bianchi, Nicolò 1963- (Author), Lugosi, Gábor 1964- (Author)
Format:	Electronic eBook
Language:	English
Published:	Cambridge Cambridge University Press 2006
Subjects:	Game theory Machine learning Computer algorithms Spieltheorie Vorhersagetheorie Maschinelles Lernen
Online Access:	BSB01 FHN01 Volltext
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
Item Description:	Title from publisher's bibliographic system (viewed on 05 Oct 2015)
Physical Description:	1 online resource (xii, 394 Seiten)
ISBN:	9780511546921
DOI:	10.1017/CBO9780511546921