Universal consistency of extreme learning machine for RBFNs case

作者：Liu Xia; Wan Anhua^*

来源：Neurocomputing, 2015, 168: 1132-1137.

DOI：10.1016/j.neucom.2015.05.010

• Extreme learning machine
• Radial basis function networks
• Universal consistency

This paper concerns the universal consistency of extreme learning machine (ELM) for radial basis function networks (RBFNs). That is, the estimator constructed by ELM for RBFNs learning system can approximate an arbitrary regression function to any accuracy, as long as the number of the training samples is sufficiently large. Furthermore, we also give the conditions for the kernel functions, with which the corresponding ELM-RBFNs estimator is strongly universal consistency. These results not only underlie the feasibility of ELM for RBFNs case, but also provide guidance of practical selection for kernel functions in ELM application.

• 出版日期2015-11-30
• 单位中山大学; 榆林学院; 西安交通大学