Abstract
Aiming at the problem of measuring the voting disagreement of committee, a minimal difference sampling method for image classification was proposed. It selects the sample with the minimal difference of two highest class probabilities voted by committee. The experimental results show that this method effectively enhances the classification accuracy compared with EQB and nEQB. Furthermore, the influence of the number of models in the decision-making committee was analyzed and discussed. The experimental results show that the proposed method always outperforms nEQB with the same number of models.
Original language | English |
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Pages (from-to) | 107-114 |
Number of pages | 8 |
Journal | Tongxin Xuebao/Journal on Communications |
Volume | 35 |
Issue number | 1 |
DOIs | |
State | Published - Jan 2014 |
Keywords
- Active learning
- Committee voting
- Image classification
- Minimal difference
- Sampling strategy