PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

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.

EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Theory & Algorithms
ID Code:1331
Deposited By:Ronald Ortner
Deposited On:28 November 2005

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