Stephen D. Scott, Jun Zhang, and Joshua Brown. On Generalized Multiple-Instance Learning. International Journal of Computational Intelligence and Applications, 5(1):21–35, March 2005.
Abstract

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 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.

Keywords: multiple-instance learning, Hausdorff metric, Winnow, content-based image retrieval, drug discovery, protein sequence identification

To Stephen D. Scott's home page

Last modified 28 March 2005.