  Multi-instance learning attempts to learn from a training set consisting of
labeled bags each containing many unlabeled instances. Previous studies
typically treat the instances in the bags as independently and identically
distributed. However, the instances in a bag are rarely independent, and
therefore a better performance can be expected if the instances are treated in
an non-i.i.d. way that exploits the relations among instances. In this paper,
we propose a simple yet effective multi-instance learning method, which regards
each bag as a graph and uses a specific kernel to distinguish the graphs by
considering the features of the nodes as well as the features of the edges that
convey some relations among instances. The effectiveness of the proposed method
is validated by experiments.
