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.
CITATION STYLE
Maron, O., & Lozano-Perez, T. (1998). A framework for multiple-instance learning. In Advances in Neural Information Processing Systems (pp. 570–576). Neural information processing systems foundation.
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