On Generalized Multiple-Instance Learning

Authors

• Stephen Scott, University of Nebraska-LincolnFollow
• Jun Zhang, University of Nebraska-LincolnFollow
• Joshua Brown, University of Nebraska-Lincoln

Date of this Version

2003

Document Type

Article

Comments

University of Nebraska-Lincoln, Computer Science and Engineering
Technical Report # TR-UNL-CSE-2003-0005

Abstract

We describe a generalization 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 list potential applications of this model (robot vision, content-based image retrieval, protein sequence identification, and drug discovery) and describe target concepts for these applications that cannot be represented in the conventional multiple-instance learning model. We then adapt a learning-theoretic algorithm for learning in this model and present empirical results.