Name
Abstract | ||
|---|---|---|
Active learning queries labels from the oracle for the most valuable instances to reduce the labeling cost. In many active learning studies, informative and representative instances are preferred because they are expected to have higher potential value for improving the model. Recently, the results in self-paced learning show that training the model with easy examples first and then gradually with harder examples can improve the performance. While informative and representative instances could be easy or hard, querying valuable but hard examples at early stage may lead to waste of labeling cost. In this paper, we propose a self-paced active learning approach to simultaneously consider the potential value and easiness of an instance, and try to train the model with least cost by querying the right thing at the right time. Experimental results show that the proposed approach is superior to state-of-the-art batch mode active learning methods. |
Year | Venue | Field |
|---|---|---|
2019 | THIRTY-THIRD AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE / THIRTY-FIRST INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE / NINTH AAAI SYMPOSIUM ON EDUCATIONAL ADVANCES IN ARTIFICIAL INTELLIGENCE | Active learning,Computer science,Oracle,Artificial intelligence,Batch processing,Machine learning |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ying-Peng Tang | 1 | 0 | 0.68 |
| Sheng-Jun Huang | 2 | 475 | 27.21 |