一种基于主动学习的SVM增量训练算法

Incremental training algorithm of SVM based on active learning

针对SVM训练学习过程中难以获得大量带有类标注样本的问题,提出一种基于距离比值不确定性抽样的主动SVM增量训练算法(DRB-ASVM),并将其应用于SVM增量训练.实验结果表明,在保证不影响分类精度的情况下,应用主动学习策略的SVM选择的标记样本数量大大低于随机选择的标记样本数量,从而降低了标记的工作量或代价,并且提高了训练速度.

To the problem that large-scale labeled samples are difficult to be acquired in the course of SVM training,the active learning strategy is used in the SVM training and an incremental training algorithm of active SVM based on the uncertainty based sampling of distance ratio is proposed in the paper. The experimental results show that the active SVM learning strategy can considerably reduce the labeled samples and costs compared to the passive learning method. And at the same time,it can ensure the accurate classification performance kept as the passive SVM and also expedite the SVM training.