2 research outputs found
Active Learning with Multiple Views
Active learners alleviate the burden of labeling large amounts of data by
detecting and asking the user to label only the most informative examples in
the domain. We focus here on active learning for multi-view domains, in which
there are several disjoint subsets of features (views), each of which is
sufficient to learn the target concept. In this paper we make several
contributions. First, we introduce Co-Testing, which is the first approach to
multi-view active learning. Second, we extend the multi-view learning framework
by also exploiting weak views, which are adequate only for learning a concept
that is more general/specific than the target concept. Finally, we empirically
show that Co-Testing outperforms existing active learners on a variety of real
world domains such as wrapper induction, Web page classification, advertisement
removal, and discourse tree parsing