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IJCAI
2003

Active Learning with Strong and Weak Views: A Case Study on Wrapper Induction

11 years 1 months ago
Active Learning with Strong and Weak Views: A Case Study on Wrapper Induction
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 (i.e., perfect learning is possible in each view). We extend the multi-view framework by introducing a novel algorithm, Aggressive Co-Testing, that exploits 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.
Ion Muslea, Steven Minton, Craig A. Knoblock
Added 31 Oct 2010
Updated 31 Oct 2010
Type Conference
Year 2003
Where IJCAI
Authors Ion Muslea, Steven Minton, Craig A. Knoblock
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