Effective selective sampling with dynamic certainty propagation
基于动态确定度传播的选择性采样

In traditional active learning, selective sampling was performed in batch mode, which neglected examples' correlation and thus inevitably brought in redundancy. This paper presented a dynamic batch sampling mode, using both the existing classification boundary and the previously labeled examples as guidance for further selection. Then it proposed a dynamic certainty propagation(DCP)algorithm for informative example selection. Experimental results demonstrate the effectiveness of selective sampling with DCP algorithm.