Kernel-based multiple-instance learning

Abstract

In recent years, the Multiple-Instance Learning (MIL) problem is becoming more and more popular in the machine learning community. Each training object (bag) of the MIL problem is a set of patterns (instances). Label information is only associated with the bags, but not with their constituent instances. Moreover, a positive bag must have at least one positive instance, but may have many neg-ative instances. Since we can only access the label information of a bag and a positive bag may have many negative instances, MIL is more challenging than the traditional supervised learning (or single-instance learning). On the other hand, it is fruitful to study MIL, since many real-world problems such as drug activity prediction are inherently MI problems which cannot be generalized well under the...[ Read more ]