Between-subclass piece-wise linear solutions in large scale kernel SVM learning

作者：

Highlights：

• SRS-SVM and HSRS-SVM are proposed to efficiently apply SVM on large training data.

• They utilize the subclass structure of data to estimate the set of support vectors.

• Results on multiple real and synthetic databases show reduction in the training time.

• Results on the LFW face database show the suitability with deep representations also.

摘要

•SRS-SVM and HSRS-SVM are proposed to efficiently apply SVM on large training data.•They utilize the subclass structure of data to estimate the set of support vectors.•Results on multiple real and synthetic databases show reduction in the training time.•Results on the LFW face database show the suitability with deep representations also.

论文关键词：Support vector machines,Subclass,Subcluster,Piece-wise linear solutions,Large scale learning

论文评审过程：Received 24 May 2018, Revised 18 March 2019, Accepted 9 April 2019, Available online 8 May 2019, Version of Record 20 June 2019.

论文官网地址：https://doi.org/10.1016/j.patcog.2019.04.012