Active Learning with Strong and Weak Views: A Case Study on Wrapper Induction
Proceedings of the Eighteenth International Joint Conference on Artificial Intelligence. Pages 415-419.
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 alg.orithms 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.
@inproceedings{ijcai2003p62,
title = {Active Learning with Strong and Weak Views: A Case Study on Wrapper Induction},
author = {Muslea, Ion and Minton, Steven and Knoblock, Craig A.},
booktitle = {Proceedings of the Eighteenth International Joint Conference on
Artificial Intelligence, {IJCAI-03}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Georg Gottlob},
pages = {415--419},
year = {2003},
url = {https://ijcai.org/proceedings/2003/62},
}
TY - CPAPER TI - Active Learning with Strong and Weak Views: A Case Study on Wrapper Induction AU - Muslea, Ion AU - Minton, Steven AU - Knoblock, Craig A. T2 - Proceedings of the Eighteenth International Joint Conference on Artificial Intelligence, IJCAI-03 PB - International Joint Conferences on Artificial Intelligence PY - 2003 SP - 415 EP - 419 UR - https://ijcai.org/proceedings/2003/62 ER -Copied
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