Exponentiated Gradient Exploration for Active Learning
Active learning strategies respond to the costly labeling task in a supervised classification by selecting the most useful unlabeled examples in training a predictive model. Many conventional active learning algorithms focus on refining the decision boundary, rather than exploring new regions that c...
| Main Author: | |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2016-01-01
|
| Series: | Computers |
| Subjects: | |
| Online Access: | http://www.mdpi.com/2073-431X/5/1/1 |
| Summary: | Active learning strategies respond to the costly labeling task in a supervised classification by selecting the most useful unlabeled examples in training a predictive model. Many conventional active learning algorithms focus on refining the decision boundary, rather than exploring new regions that can be more informative. In this setting, we propose a sequential algorithm named exponentiated gradient (EG)-active that can improve any active learning algorithm by an optimal random exploration. Experimental results show a statistically-significant and appreciable improvement in the performance of our new approach over the existing active feedback methods. |
|---|---|
| ISSN: | 2073-431X |