Introduction to the Special Issue on Advanced Approaches for Multiple Instance Learning on Multimedia Applications

Multiple instance learning (MIL) is concerned with learning from sets (bags) of objects (instances), where the individual instance labels are ambiguous. In this setting, supervised learning cannot be applied directly. Often, specialized MIL methods ...

The characteristics specific of MIL problems are formally identified and described.MIL methods and applications are reviewed in the light of the problem characteristics.Comparative experiments show the impact of problem characteristics on 16 reference ...

In multiple-Instance Learning (MIL), training class labels are attached to sets of bags composed of unlabeled instances, and the goal is to deal with classification of bags. Most previous MIL algorithms, which tackle classification problems, consider ...