Sciweavers

Share
NIPS
2003

Multiple-Instance Learning via Disjunctive Programming Boosting

12 years 2 months ago
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.
Stuart Andrews, Thomas Hofmann
Added 31 Oct 2010
Updated 31 Oct 2010
Type Conference
Year 2003
Where NIPS
Authors Stuart Andrews, Thomas Hofmann
Comments (0)
books