Prediction, Learning, and Games - Rilegato

Cesa-Bianchi, Nicolo; Lugosi, Gabor

Rilegato

ISBN 10: 0521841089 ISBN 13: 9780521841085

Casa editrice: Cambridge University Press, 2006

Riassunto

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.

Su questo libro

Recensione:

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

Descrizione del libro:

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.

Su questa edizione

• Editore: Cambridge University Press
• Data di pubblicazione: 2006
• ISBN 10: 0521841089
• ISBN 13: 9780521841085
• Rilegatura: Copertina rigida
• Numero di pagine: 408