Machine Learning, Game Theory & Study, Mathematical Programming & Operations Research, Programming - General & Miscellaneous

Prediction, Learning, and Games

Name: Prediction, Learning, and Games
Author: Nicolo Cesa-Bianchi
ISBN: 9780521841085

by Nicolo Cesa-Bianchi, Gabor Lugosi

Available on Bookshop Write a review

Books.org participates in affiliate programs including Bookshop.org and the Amazon Services LLC Associates Program. We may earn a commission from qualifying purchases made through links on this page, at no additional cost to you.

Overview

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 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.

Synopsis

Prediction using expert advice provides a general framework for repeated game playing, adaptive data compression, sequential investments, sequential pattern analysis and other problems.

About the Author, Nicolo Cesa-Bianchi

Nicol- 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 the acting 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.

Reviews

There are no reviews yet. Log in to write one.

Editorials

From the Publisher

"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