A variance maximization criterion for active learning

作者：

Highlights：

• A novel active learning approach is proposed, which measures the value of unlabeled data by its predictive variance.

• To measure the informativeness and representativeness of unlabeled instances, two types of variance are introduced.

• Excellent performance of the proposed method in comparison with state-of-the-art active learning algorithms is demonstrated.

摘要

•A novel active learning approach is proposed, which measures the value of unlabeled data by its predictive variance.•To measure the informativeness and representativeness of unlabeled instances, two types of variance are introduced.•Excellent performance of the proposed method in comparison with state-of-the-art active learning algorithms is demonstrated.

论文关键词：Active learning,Retraining information matrix,Variance maximization

论文评审过程：Received 23 June 2017, Revised 9 January 2018, Accepted 22 January 2018, Available online 31 January 2018, Version of Record 10 February 2018.

论文官网地址：https://doi.org/10.1016/j.patcog.2018.01.017