Multiple Instance Learning with Generalized Support Vector Machines

Book One

Volume

Proceedings of the AAAI Conference on Artificial Intelligence, 18

Student Abstracts

In pattern classification it is usually assumed that a training set of patterns along with their class labels is available. Multiple-Instance Learning (MIL) generalizes this problem setting by making weaker assumptions about the labeling information. We propose to generalize Support Vector Machines to take into account such weak labeling of the type found in MIL. Our method is able to identify superior discriminant functions, as is demonstrated in experiments on synthetic and image datasets.