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.

• Type: Conference
• Year: 1997
• Where: NIPS
• Authors: Oded Maron, Tomás Lozano-Pérez
• Keywords: Multiple-Instance Learning, Negative Bags, NIPS 1997, NIPS 2007, Supervised Learning