一种基于少样本数据的在线主动学习与分类方法
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杨静, 赵文仓, 徐越, 冯旸赫, 黄金才
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An on⁃line active learning and classification method based on small sample data
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Jing Yang, Wencang Zhao, Yue Xu, Yanghe Feng, Jincai Huang
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表1 MNIST手写数据集的识别结果
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Table 1 Recognition result for MNIST dataset
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| 方法 | T9 | T16 | T23 | T30 | T44 | T51 | T58 | T65 | T72 | T79 | T86 | T93 | T100 |
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| 变异比 | 83.75% | 87.68% | 90.87% | 93.13% | 93.03% | 94.96% | 94.97% | 95.15% | 95.80% | 95.54% | 94.96% | 96.13% | 96.59% | | 预测熵 | 83.53% | 85.04% | 90.30% | 91.05% | 95.09% | 94.67% | 96.23% | 97.11% | 96.93% | 97.94% | 96.85% | 98.03% | 98.01% | | BALD | 86.73% | 89.06% | 93.75% | 92.77% | 95.85% | 95.20% | 96.76% | 97.39% | 97.48% | 98.04% | 98.32% | 98.20% | 98.15% | | CISs | 93.25% | 94.93% | 95.87% | 95.13% | 96.03% | 96.96% | 97.97% | 99.15% | 99.80% | 99.54% | 99.96% | 99.13% | 99.25% |
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