The performance of ELM based ridge regression via the regularization parameters
作者：
Highlights：
• Biased estimators may be powerful tools in extreme learning machine studies.
• Ridge based regression estimators may outperform the extreme machine learning.
• Choice of ridge regularization parameter is effective to obtain more stable results.
• Generalization performance of ELM depend on selection of regularization parameter.
摘要
•Biased estimators may be powerful tools in extreme learning machine studies.•Ridge based regression estimators may outperform the extreme machine learning.•Choice of ridge regularization parameter is effective to obtain more stable results.•Generalization performance of ELM depend on selection of regularization parameter.
论文关键词：Extreme learning machine,Ridge regression,Almost unbiased ridge regression,Regularized extreme learning machine,Model selection
论文评审过程：Received 26 July 2018, Revised 7 May 2019, Accepted 25 May 2019, Available online 27 May 2019, Version of Record 7 June 2019.
论文官网地址：https://doi.org/10.1016/j.eswa.2019.05.039
