Prediction, Learning, and Games
Author(s): Nicolo Cesa-Bianchi (Author), Gabor Lugosi (Author)
- Publisher: Cambridge University Press
- Publication Date: March 13, 2006
- Edition: 1st
- Language: English
- Print length: 408 pages
- ISBN-10: 0521841089
- ISBN-13: 9780521841085
Book Description
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.
Editorial Reviews
Review
“Each chapter contains about two dozen inspiring problems, and the book refers to more than 300 up-to-date sources…. The book is addressed to graduate students and researchers in the fields of engineering and information, computer sciences, and data analysis; it presents both theoretical inference and practical hands-on usage of modern prediction techniques.”
Stan Lipovektsy, GfK Custom Research North AmericaTechnometrics
“This book is a comprehensive treatment of current results on predicting using expert advice.”
David S. Leslie, Mathematical Reviews
Book Description
This book provides a general framework for repeated game playing, adaptive data compression, sequential investments, sequential pattern analysis and other problems.
About the Author
Nicolò Cesa-Bianchi is Professor of Computer Science at the University of Milan, Italy. His research interests include learning theory, pattern analysis, and worst-case analysis of algorithms. He is action editor of The Machine Learning Journal. Gábor Lugosi has been working on various problems in pattern classification, nonparametric statistics, statistical learning theory, game theory, probability, and information theory. He is co-author of the monographs, A Probabilistic Theory of Pattern Recognition and Combinatorial Methods of Density Estimation. He has been an associate editor of various journals including The IEEE Transactions of Information Theory, Test, ESAIM: Probability and Statistics and Statistics and Decisions.
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