A Framework for Multiple-Instance Learning

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A Framework for Multiple-Instance Learning
Multiple-instance learning is a variation on supervised learning, where the task is to learn a concept given positive and negative bags of instances. Each bag may contain many instances, but a bag is labeled positive even if only one of the instances in it falls within the concept. A bag is labeled negative only if all the instances in it are negative. We describe a new general framework, called Diverse Density, for solving multiple-instance learning problems. We apply this framework to learn a simple description of a person from a series of images (bags) containing that person, to a stock selection problem, and to the drug activity prediction problem.
Oded Maron, Tomás Lozano-Pérez
Added 01 Nov 2010
Updated 01 Nov 2010
Type Conference
Year 1997
Where NIPS
Authors Oded Maron, Tomás Lozano-Pérez
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