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2001
Springer

Real-Valued Multiple-Instance Learning with Queries

9 years 4 months ago
Real-Valued Multiple-Instance Learning with Queries
While there has been a significant amount of theoretical and empirical research on the multiple-instance learning model, most of this research is for concept learning. However, for the important application area of drug discovery, a real-valued classification is preferable. In this paper we initiate a theoretical study of real-valued multiple-instance learning. We prove that the problem of finding a target point consistent with a set of labeled multiple-instance examples (or bags) is NP-complete, and that the problem of learning from real-valued multiple-instance examples is as hard as learning DNF. Another contribution of our work is in defining and studying a multiple-instance membership query (MI-MQ). We give a positive result on exactly learning the target point for a multiple-instance problem in which the learner is provided with a MI-MQ oracle and a single adversarially selected bag. © 2005 Elsevier Inc. All rights reserved.
Daniel R. Dooly, Sally A. Goldman, Stephen Kwek
Added 15 Mar 2010
Updated 15 Mar 2010
Type Conference
Year 2001
Where ALT
Authors Daniel R. Dooly, Sally A. Goldman, Stephen Kwek
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