An active learning-based SVM multi-class classification model

作者：

Highlights：

• The proposed MC_SVMA model is a deep fusion of active learning and multi-classification.

• The MC_SVMA model can mine categories from given unlabeled samples quickly with much less labeling cost.

• Those difficult distinguished unlabeled samples can be classified and new categories may be found.

• The MC_SVMA model can obtain fast learning and good performance for unlabeled multiple classification problems.

摘要

•The proposed MC_SVMA model is a deep fusion of active learning and multi-classification.•The MC_SVMA model can mine categories from given unlabeled samples quickly with much less labeling cost.•Those difficult distinguished unlabeled samples can be classified and new categories may be found.•The MC_SVMA model can obtain fast learning and good performance for unlabeled multiple classification problems.

论文关键词：Multi-class classification with unknown categories,Active learning,Support vector machine,MC_SVMA model

论文评审过程：Received 21 May 2014, Revised 18 November 2014, Accepted 6 December 2014, Available online 18 December 2014.

论文官网地址：https://doi.org/10.1016/j.patcog.2014.12.009