The weighted majority algorithm
Information and Computation
Efficient algorithms for online decision problems
Journal of Computer and System Sciences - Special issue: Learning theory 2003
Learning to classify by ongoing feature selection
CRV '06 Proceedings of the The 3rd Canadian Conference on Computer and Robot Vision
Prediction, Learning, and Games
Prediction, Learning, and Games
Multitask learning with expert advice
COLT'07 Proceedings of the 20th annual conference on Learning theory
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We consider the problem of choosing, sequentially, a map which assigns elements of a set A to a few elements of a set B. On each round, the algorithm suffers some cost associated with the chosen assignment, and the goal is to minimize the cumulative loss of these choices relative to the best map on the entire sequence. Even though the offline problem of finding the best map is provably hard, we show that there is an equivalent online approximation algorithm, Randomized Map Prediction (RMP), that is efficient and performs nearly as well. While drawing upon results from the "Online Prediction with Expert Advice" setting, we show how RMP can be utilized as an online approach to several standard batch problems. We apply RMP to online clustering as well as online feature selection and, surprisingly, RMP often outperforms the standard batch algorithms on these problems.