Abstract

Multi-view learners reduce the need for labeled data by exploiting disjoint sub-sets of features (views), each of which is sufficient for learning. Such algorithms assume that each view is a strong view (ie, perfect learning is possible in each view). We extend the multi-view framework by introducing a novel algorithm, Aggressive Co-Testing, that ex-ploits both strong and weak views; in a weak view, one can learn a concept that is strictly more general or specific than the target concept. Aggressive Co-Testing uses the weak views both for detecting the most informative examples in the domain and for improving the accuracy of the predictions. In a case study on 33 wrapper induction tasks, our algorithm requires significantly fewer labeled examples than existing state-of-the-art approaches.

Date

2003

Authors

Ion Muslea, Steven N Minton, Craig A Knoblock

Journal

IJCAI: Proceedings of the Conference

Pages

415

Publisher

IJCAI