A Boosting Approach to Multiple Instance Learning

  • Peter Auer
  • Ronald Ortner
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3201)

Abstract

In this paper we present a boosting approach to multiple instance learning. As weak hypotheses we use balls (with respect to various metrics) centered at instances of positive bags. For the ∞-norm these hypotheses can be modified into hyper-rectangles by a greedy algorithm. Our approach includes a stopping criterion for the algorithm based on estimates for the generalization error. These estimates can also be used to choose a preferable metric and data normalization. Compared to other approaches our algorithm delivers improved or at least competitive results on several multiple instance benchmark data sets.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Peter Auer
    • 1
  • Ronald Ortner
    • 1
  1. 1.University of LeobenLeobenAustria

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