Does one rotten apple spoil the whole barrel?

摘要

Multiple Instance Learning (MIL) is concerned with learning from sets (bags) of objects (instances), where the individual instance labels are ambiguous. In MIL it is often assumed that positive bags contain at least one instance from a so-called concept in instance space. However, there are many MIL problems that do not fit this formulation well, and hence cause traditional MIL algorithms, which focus on the concept, to perform poorly. In this work we show such types of problems and the methods appropriate to deal with either situation. Furthermore, we show that an approach that learns directly from dissimilarities between bags can be adapted to deal with either problem.