1995 | ReviewPaper | Buchkapitel
A desicion-theoretic generalization of on-line learning and an application to boosting
verfasst von : Yoav Freund, Robert E. Schapire
Erschienen in: Computational Learning Theory
Verlag: Springer Berlin Heidelberg
Enthalten in: Professional Book Archive
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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 rule of Littlestone and Warmuth [10] can be adapted to this mode yielding bounds that are slightly weaker in some cases, but applicable to a considerably more general class of learning problems. We show how the resulting learning algorithm can be applied to a variety of problems, including gambling, multiple-outcome prediction, repeated games and prediction of points in ℝn. We also show how the weight-update rule can be used to derive a new boosting algorithm which does not require prior knowledge about the performance of the weak learning algorithm.