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 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.

Authors

• Stuart Andrews Thomas Hofmann
• Thomas Hofmann ETH Zürich

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