# A decision-theoretic generalization of on-line learning and an application to boosting

@inproceedings{Freund1995ADG, title={A decision-theoretic generalization of on-line learning and an application to boosting}, author={Yoav Freund and Robert E. Schapire}, booktitle={EuroCOLT}, year={1995} }

In the first part of the paper we consider the problem of dynamically apportioning resources among a set of options in a worst-case on-line framework. The model we study can be interpreted as a broad, abstract extension of the well-studied on-line prediction model to a general decision-theoretic setting. We show that the multiplicative weight-update Littlestone?Warmuth rule can be adapted to this model, yielding bounds that are slightly weaker in some cases, but applicable to a considerably…

## 18,043 Citations

### Potential-Based Algorithms in On-Line Prediction and Game Theory

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This paper shows that several known algorithms for sequential prediction problems, for playing iterated games, and for boosting are special cases of a general decision strategy based on the notion of potential, and describes a notion of generalized regret and its applications in learning theory.

### On Boosting with Optimal Poly-Bounded Distributions

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### Potential-Based Algorithms in On-Line Prediction and Game Theory ∗

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