{"type":"ScholarlyArticle","title":"Active Learning with Strong and Weak Views: A Case Study on Wrapper Induction","authors":[{"name":"Ion Muslea","slug":"ion-muslea","given":"Ion","family":"Muslea","affiliation":""},{"name":"Steven Minton","slug":"steven-minton","given":"Steven","family":"Minton","affiliation":""},{"name":"Craig A. Knoblock","slug":"craig-a-knoblock","given":"Craig A.","family":"Knoblock","affiliation":""}],"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.","doi":null,"url":"https://ijcai.org/proceedings/2003/62","pdf":"https://ijcai.org/proceedings/2003/0062.pdf","markdown":"https://ijcai.org/proceedings/2003/62.md","bibtex":"https://ijcai.org/proceedings/2003/bibtex/62","volume":{"slug":"2003","title":"Proceedings of the Eighteenth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2003","url":"https://ijcai.org/proceedings/2003/","year":2003,"location":"Acapulco, Mexico","dates":"","editors":"Georg Gottlob","isbn":"","publisher":"International Joint Conferences on Artificial Intelligence","doi_prefix":""},"section":null,"track":"","number":62,"pages":{"first":415,"last":419},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}