Multiple-Instance Learning via Disjunctive Programming Boosting

Learning from ambiguous training data is highly relevant in many applications. We present a new learning algorithm for classification problems where labels are associated with sets of pattern instead of individual patterns. This encompasses multiple instance learning as a special case. Our approach is based on a generalization of linear programming boosting and uses results from disjunctive programming to generate successively stronger linear relaxations of a discrete non-convex problem.

• Type: Conference
• Year: 2003
• Where: NIPS
• Authors: Stuart Andrews, Thomas Hofmann
• Keywords: Linear Programming Boosting, Multiple Instance Learning, NIPS 2003, NIPS 2007, Stronger Linear Relaxations