Prediction, Learning, and Games

Description

Prediction, Learning, and Games  

By Nicolo Cesa-Bianchi, Gabor Lugosi,

• Publisher:   Cambridge University Press
• Number Of Pages:   406
• Publication Date:   2006-03-13
• Sales Rank:   113117
• ISBN / ASIN:   0521841089
• EAN:   9780521841085
• Binding:   Hardcover
• Manufacturer:   Cambridge University Press
• Studio:   Cambridge University Press
• Average Rating:  
• Total Reviews:  

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Book Description:

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 freeduan.com 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.Password: gigapedia.org
Size: 2.39 MB
MD5: ba76efdd5221a2005cf5fd6884038cc9

http://rapidshare.com/files/32869198/0521841089.zip.html

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