Regularized minimax probability machine

作者：

Highlights：

• Novel robust approach for classification using nonlinear second-order cone programming.

• The regularized version for Minimax Probability Machine is proposed.

• A geometrically grounded method based on the concept of ellipsoids.

• Superior performance is achieved in experiments on benchmark datasets.

摘要

•Novel robust approach for classification using nonlinear second-order cone programming.•The regularized version for Minimax Probability Machine is proposed.•A geometrically grounded method based on the concept of ellipsoids.•Superior performance is achieved in experiments on benchmark datasets.

论文关键词：Minimax probability machine,Regularization,Second-order cone programming,Support vector machines

论文评审过程：Received 18 October 2018, Revised 2 January 2019, Accepted 19 April 2019, Available online 25 April 2019, Version of Record 22 May 2019.

论文官网地址：https://doi.org/10.1016/j.knosys.2019.04.016