Name
Abstract | ||
|---|---|---|
The goal of online active learning is to learn predictive models from a sequence of unlabeled data given limited label query budget. Unlike conventional online learning tasks, online active learning is considerably more challenging because of two reasons. First, it is difficult to design an effective query strategy to decide when is appropriate to query the label of an incoming instance given limi... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/TKDE.2017.2778097 | IEEE Transactions on Knowledge and Data Engineering |
Keywords | Field | DocType |
Algorithm design and analysis,Prediction algorithms,Predictive models,Labeling,Machine learning algorithms,Training | Online learning,Training set,Convergence (routing),Algorithm design,Active learning,Computer science,Synchronous learning,Artificial intelligence,Machine learning,Empirical research,Scalability | Journal |
Volume | Issue | ISSN |
30 | 7 | 1041-4347 |
Citations | PageRank | References |
4 | 0.40 | 0 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shuji Hao | 1 | 110 | 6.57 |
| Jing Lu | 2 | 55 | 4.74 |
| Peilin Zhao | 3 | 1365 | 80.09 |
| Chi Zhang | 4 | 6 | 1.78 |
| Steven C. H. Hoi | 5 | 268 | 17.70 |
| Chunyan Miao | 6 | 2307 | 195.72 |