一种基于少样本数据的在线主动学习与分类方法
|
|
杨静, 赵文仓, 徐越, 冯旸赫, 黄金才
|
An on⁃line active learning and classification method based on small sample data
|
|
Jing Yang, Wencang Zhao, Yue Xu, Yanghe Feng, Jincai Huang
|
|
表2 CIFAR?10的识别结果
|
Table 2 Recognition result for CIFAR?10 dataset
|
|
| 方法 | T9 | T16 | T23 | T30 | T44 | T51 | T58 | T65 | T72 | T79 | T86 | T93 | T100 |
|---|
| 变异比 | 51.92% | 55.38% | 57.68% | 59.54% | 60.98% | 62.32% | 63.75% | 64.29% | 64.29% | 65.99% | 66.71% | 67.48% | 68.12% | | 预测熵 | 49.96% | 53.55% | 56.05% | 58.38% | 60.14% | 62.18% | 63.45% | 65.07% | 66.27% | 67.12% | 68.26% | 69.14% | 69.53% | | BALD | 49.67% | 53.47% | 56.96% | 58.92% | 60.41% | 61.95% | 63.72% | 64.56% | 66.09% | 67.17% | 67.36% | 69.15% | 69.90% | | CISs | 56.19% | 60.69% | 63.36% | 66.08% | 67.38% | 69.18% | 69.75% | 71.18% | 71.97% | 72.23% | 72.46% | 73.45% | 73.75% |
|
|
|