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Abstract:
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.
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