A Game of Prediction with Expert Advice
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In this paper, the authors consider the following problem: at each point of discrete time the learner must make a prediction; he is given the predictions made by a pool of experts.About:
This article is published in Journal of Computer and System Sciences.The article was published on 1998-04-01 and is currently open access. It has received 284 citations till now. The article focuses on the topics: Outcome (game theory).read more
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Prediction, learning, and games
Nicolò Cesa-Bianchi,Gábor Lugosi +1 more
TL;DR: In this paper, the authors provide a comprehensive treatment of the problem of predicting individual sequences using expert advice, a general framework within which many related problems can be cast and discussed, such as repeated game playing, adaptive data compression, sequential investment in the stock market, sequential pattern analysis, and several other problems.
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A survey on concept drift adaptation
TL;DR: The survey covers the different facets of concept drift in an integrated way to reflect on the existing scattered state of the art and aims at providing a comprehensive introduction to the concept drift adaptation for researchers, industry analysts, and practitioners.
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Adaptive game playing using multiplicative weights
Yoav Freund,Robert E. Schapire +1 more
TL;DR: A variant of the game-playing algorithm is proved to be optimal in a very strong sense and a new, simple proof of the min–max theorem, as well as a provable method of approximately solving a game.
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Tracking the Best Expert
Mark Herbster,Manfred K. Warmuth +1 more
TL;DR: The generalization allows the sequence to be partitioned into segments, and the goal is to bound the additional loss of the algorithm over the sum of the losses of the best experts for each segment to model situations in which the examples change and different experts are best for certain segments of the sequence of examples.
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On the generalization ability of on-line learning algorithms
TL;DR: This paper proves tight data-dependent bounds for the risk of this hypothesis in terms of an easily computable statistic M/sub n/ associated with the on-line performance of the ensemble, and obtains risk tail bounds for kernel perceptron algorithms interms of the spectrum of the empirical kernel matrix.