Name
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 (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. |
Year | Venue | Keywords |
|---|---|---|
2003 | IJCAI | target concept,perfect learning,wrapper induction,weak view,active learning,disjoint sub-sets,novel algorithm,strong view,informative example,case study,multi-view learner,aggressive co-testing |
Field | DocType | Citations |
Active learning,Disjoint sets,Computer science,Exploit,Artificial intelligence,Labeled data,Machine learning | Conference | 37 |
PageRank | References | Authors |
1.38 | 14 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ion Muslea | 1 | 1344 | 121.66 |
| Steven Minton | 2 | 3473 | 536.74 |
| Craig A. Knoblock | 3 | 5229 | 680.57 |