PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Prediction, Learning, and Games
Nicolò Cesa-Bianchi and Gábor Lugosi
(2006) Cambridge University Press , New York, USA . ISBN 0521841089

Abstract

This book 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 with the purpose of characterizing their theoretical limitations and possibilities.

EPrint Type:Book
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Theory & Algorithms
ID Code:2143
Deposited By:Nicolò Cesa-Bianchi
Deposited On:06 July 2006