Stuart Andrews, Thomas Hofmann
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 learn- ing 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.