Multi-parameter safe sample elimination rule for accelerating nonlinear multi-class support vector machines
作者：
Highlights：
• A safe sample elimination rule (SSE) for the multi-class models K-SVCR and Twin-KSVC is presented based on variational inequality.
• Our SSE can delete the redundant samples to accelerate the computational speed.
• Our SSE guarantees the solution to be exactly the same with the original problem.
• Our SSE is not only efficient for single parameter case but also for multi-parameter case.
摘要
•A safe sample elimination rule (SSE) for the multi-class models K-SVCR and Twin-KSVC is presented based on variational inequality.•Our SSE can delete the redundant samples to accelerate the computational speed.•Our SSE guarantees the solution to be exactly the same with the original problem.•Our SSE is not only efficient for single parameter case but also for multi-parameter case.
论文关键词：K-SVCR,Twin-KSVC,Safe elimination rule,Multi-class classification,Multi-parameter
论文评审过程：Received 8 August 2018, Revised 3 January 2019, Accepted 26 May 2019, Available online 27 May 2019, Version of Record 4 June 2019.
论文官网地址：https://doi.org/10.1016/j.patcog.2019.05.037
