COLT '90 Proceedings of the third annual workshop on Computational learning theory
STOC '93 Proceedings of the twenty-fifth annual ACM symposium on Theory of computing
Minimax learning in iterated games via distributional majorization
Minimax learning in iterated games via distributional majorization
How to use expert advice in the case when actual values of estimated events remain unknown
COLT '95 Proceedings of the eighth annual conference on Computational learning theory
Journal of the ACM (JACM)
On-line learning and the metrical task system problem
COLT '97 Proceedings of the tenth annual conference on Computational learning theory
On-line Learning and the Metrical Task System Problem
Machine Learning
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We consider a game theoretic approach for sequentially choosing decisions by combining the suggestions of a fixed number of experts. Since the optimal decision making strategy is often computationally expensive, we present a methodology for obtaining approximate strategies with provably good performance. These methods are applicable to any decision problem with bounded payoff function, are computationally feasible, and arise naturally as approximations to the exact solution. We illustrate the ideas by applying our results to the problem of predicting a sequence of letters drawn from a finite alphabet with the goal being to minimize the number of mistakes made.