A Boosting Approach to Multiple Instance Learning

  • Peter Auer
  • Ronald Ortner
Conference paper

DOI: 10.1007/978-3-540-30115-8_9

Part of the Lecture Notes in Computer Science book series (LNCS, volume 3201)
Cite this paper as:
Auer P., Ortner R. (2004) A Boosting Approach to Multiple Instance Learning. In: Boulicaut JF., Esposito F., Giannotti F., Pedreschi D. (eds) Machine Learning: ECML 2004. ECML 2004. Lecture Notes in Computer Science, vol 3201. Springer, Berlin, Heidelberg

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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