| Year | Title | Author | Publication | Code | Tasks | Notes | Datasets | Notions |
|---|---|---|---|---|---|---|---|---|
| 2019 | Automatic Tuning of the RBF Kernel Parameter for Batch-Mode Active Learning Algorithms: A Scalable Framework | Chang and Huang | IEEE CYB | - | Image Classification |
lower bound of the hypothesis margin, RBF kernel function, None, Tra, Hard |
PIX, ORL, MNIST, UCI, | for automatic tuning of the kernel parameter, a hypothesis-margin-based criterion function is proposed. Three frameworks are also developed to incorporate the function of automatic tuning of the kernel parameter with existing batch- model active learning algorithms. |