A boosting approach to multiple instance learning
Peter Auer and Ronald Ortner
Journal of Machine Learning Research 2005.

This is the latest version of this eprint.

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 metric induced by the infinity-norm these hypotheses can be modified into hyper-rectangles by a greedy algorithm. Compared to other approaches our algorithm delivers improved or at least competitive results on several multiple-instance benchmark data sets.

Available Versions of this Item

• A boosting approach to multiple instance learning (deposited 25 December 2004)
  • A boosting approach to multiple instance learning (deposited 28 November 2005) [Currently Displayed]

[Edit]