Multi-Class Active Learning by Integrating Uncertainty and Diversity
Active learning is a promising way to reduce the labeling cost with a limited training samples initially, and then iteratively select the most valuable samples from a large number of unlabeled data for labeling in order to construct a powerful classifier. The goal of active learning is to make the l...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2018-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8320778/ |