The following publications are possibly variants of this publication:
- Fast Extended One-Versus-Rest Multi-label SVM Classification Algorithm Based on Approximate Extreme PointsZhongwei Sun, Zhongwen Guo, Xupeng Wang, Jing Liu, Shiyong Liu. dasfaa 2017: 265-278 [doi]
- Research and Application of Fast Multi-label SVM Classification Algorithm Using Approximate Extreme PointsZhongwei Sun, Zhongwen Guo, Mingxing Jiang, Xi Wang, Chao Liu. bigcom 2016: 39-52 [doi]
- Fast multi-label SVM training based on approximate extreme pointsZhongwei Sun, Zhongwen Guo, Chao Liu 0008, Mingxing Jiang, Xi Wang 0003. ida, 22(5):1079-1099, 2018. [doi]
- An Efficient Multi-Label SVM Classification Algorithm by Combining Approximate Extreme Points Method and Divide-and-Conquer StrategyZhongwei Sun, Xiuyan Liu, Keyong Hu, Zhuang Li, Jing Liu. access, 8:170967-170975, 2020. [doi]
- A weight set decomposition algorithm for finding all efficient extreme points in the outcome set of a multiple objective linear programHarold P. Benson, Erjiang Sun. eor, 139(1):26-41, 2002. [doi]