Name
Abstract | ||
|---|---|---|
Active learners alleviate the burden of labeling large amounts of data by detecting and asking the user to label only the most informative examples in the domain. We focus here on active learning for multi-view domains, in which there are several disjoint subsets of features (views), each of which is sufficient to learn the target concept. In this paper we make several contributions. First, we introduce Co-Testing, which is the first approach to multi-view active learning. Second, we extend the multi-view learning framework by also exploiting weak views, which are adequate only for learning a concept that is more general/specific than the target concept. Finally, we empirically show that Co-Testing outperforms existing active learners on a variety of real world domains such as wrapper induction, Web page classification, advertisement removal, and discourse tree parsing. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1613/jair.2005 | Encyclopedia of Data Warehousing and Mining |
Keywords | Field | DocType |
target concept,multiple view,multi-view domain,real world,active learning,large amount,disjoint subsets,discourse tree parsing,informative example,advertisement removal,active learner | Active learning,Semi-supervised learning,Disjoint sets,Active learning (machine learning),Web page,Computer science,Artificial intelligence,Parsing,Machine learning | Journal |
Volume | Issue | ISSN |
27 | 1 | Journal Of Artificial Intelligence Research, Volume 27, pages
203-233, 2006 |
Citations | PageRank | References |
76 | 2.41 | 66 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ion Muslea | 1 | 1344 | 121.66 |
| Steven Minton | 2 | 3473 | 536.74 |
| Craig A. Knoblock | 3 | 5229 | 680.57 |