Publication Date

2007

Subjects

Computing Methodologies

Abstract

We propose a new generalized multiple-instance learning (MIL) algorithm, MICCLLR (multiple-instance class conditional likelihood ratio), that converts the MI data into a single meta-instance data allowing any propositional classifier to be applied. Experimental results on a wide range of MI data sets show that MICCLLR is competitive with some of the best performing MIL algorithms reported in literature.

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