Name
Abstract | ||
|---|---|---|
Active learning efficiently hones in on the decision boundary between relevant and irrelevant documents, but in the process can miss entire clusters of relevant documents, yielding classifiers with low recall. In this paper, we propose a method to increase active learning recall by constraining sampling to a document subset rich in relevant examples. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1145/1277741.1277962 | SIGIR |
Keywords | Field | DocType |
decision boundary,relevant document,document subset,improving active learning recall,irrelevant document,active learning recall,disjunctive boolean constraint,entire cluster,low recall,relevant example,active learning | Data mining,Active learning,Active learning (machine learning),Information retrieval,Computer science,Artificial intelligence,Sampling (statistics),Recall,Decision boundary,Machine learning | Conference |
Citations | PageRank | References |
0 | 0.34 | 9 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Emre Velipasaoglu | 1 | 133 | 6.61 |
| Hinrich Schütze | 2 | 2113 | 362.21 |
| Jan O. Pedersen | 3 | 6301 | 1177.07 |