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. 1 Introduction One of the drawbacks of applying the supervised learning model is that it is not always possible for a teacher to provide labeled examples for training. Multiple-instance learning provides a new way of modeling the teacher's weakness. Instead of receiving a set of instances which are labeled positive or negative, the lear...

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