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Prediction, Learning, and Games:


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Table of Contents

1. Introduction; 2. Prediction with expert advice; 3. Tight bounds for specific losses; 4. Randomized prediction; 5. Efficient forecasters for large classes of experts; 6. Prediction with limited feedback; 7. Prediction and playing games; 8. Absolute loss; 9. Logarithmic loss; 10. Sequential investment; 11. Linear pattern recognition; 12. Linear classification; 13. Appendix.

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This book 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 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. Gabor 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.


"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 America Technometrics, May 2007

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