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.

This book constitutes the refereed proceedings of the 13th International Conference on Algorithmic Learning Theory, ALT 2002, held in Lübeck, Germany in November 2002. The 26 revised full papers presented together with 5 invited contributions and an introduction were carefully reviewed and selected from 49 submissions. The papers are organized in topical sections on learning Boolean functions, boosting and margin-based learning, learning with queries, learning and information extraction, inductive inference, inductive logic programming, language learning, statistical learning, and applications and heuristics.