Bayesian robust multi-extreme learning machine

作者：

Highlights：

• Two robust regression models are designed based on multi-extreme learning machine.

• Two infinite mixture distributions make the proposed models more robust.

• Sparse Bayesian learning is employed to make the proposed models more compact.

• The proposed models are tested comprehensively on different kinds of datasets.

摘要

•Two robust regression models are designed based on multi-extreme learning machine.•Two infinite mixture distributions make the proposed models more robust.•Sparse Bayesian learning is employed to make the proposed models more compact.•The proposed models are tested comprehensively on different kinds of datasets.

论文关键词：Multi-extreme learning machine,Infinite mixture of Gaussians,Infinite mixture of Student’s t-distributions,Variational Bayesian,Sparse priors,Robust regression

论文评审过程：Received 4 May 2020, Revised 28 July 2020, Accepted 1 September 2020, Available online 8 October 2020, Version of Record 12 October 2020.

论文官网地址：https://doi.org/10.1016/j.knosys.2020.106468