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

  • Yoav Freund
  • Robert E. Schapire
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 904)

Abstract

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 ℝ n . 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.

Keywords

Loss Function Weak Hypothesis Algorithm AdaBoost Final Hypothesis Cumulative Loss 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 1995

Authors and Affiliations

  • Yoav Freund
    • 1
  • Robert E. Schapire
    • 1
  1. 1.AT&T Bell LaboratoriesMurray Hill

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