A regularization framework for multiple-instance learning

This paper focuses on kernel methods for multi-instance learning. Existing methods require the prediction of the bag to be identical to the maximum of those of its individual instances. However, this is too restrictive as only the sign is important in classification. In this paper, we provide a more... View More