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ON GENERALIZED MULTIPLE-INSTANCE LEARNING

STEPHEN SCOTT

Department of Computer Science, 256 Avery Hall, University of Nebraska, Lincoln, NE 68588-0115, USA

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JUN ZHANG

Department of Computer Science, 256 Avery Hall, University of Nebraska, Lincoln, NE 68588-0115, USA

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, and

JOSHUA BROWN

Department of Mathematics, 203 Avery Hall, University of Nebraska, Lincoln, NE 68588-0323, USA

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https://doi.org/10.1142/S1469026805001453Cited by:26 (Source: Crossref)

Abstract

We describe a generalisation of the multiple-instance learning model in which a bag's label is not based on a single instance's proximity to a single target point. Rather, a bag is positive if and only if it contains a collection of instances, each near one of a set of target points. We then adapt a learning-theoretic algorithm for learning in this model and present empirical results on data from robot vision, content-based image retrieval, and protein sequence identification.

A more detailed, preliminary version of this paper appears as technical report UNL-CSE-2003-5, Department of Computer Science, University of Nebraska (2003).

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