  We consider active, semi-supervised learning in an offline transductive
setting. We show that a previously proposed error bound for active learning on
undirected weighted graphs can be generalized by replacing graph cut with an
arbitrary symmetric submodular function. Arbitrary non-symmetric submodular
functions can be used via symmetrization. Different choices of submodular
functions give different versions of the error bound that are appropriate for
different kinds of problems. Moreover, the bound is deterministic and holds for
adversarially chosen labels. We show exactly minimizing this error bound is
NP-complete. However, we also introduce for any submodular function an
associated active semi-supervised learning method that approximately minimizes
the corresponding error bound. We show that the error bound is tight in the
sense that there is no other bound of the same form which is better. Our
theoretical results are supported by experiments on real data.
