A Survey of Online Sequential Extreme Learning Machine

Abstract

Online sequential extreme learning machine (OS-ELM) can learn the data one-by-one or chunk-by-chunk with the fixed or varying chunk size. It was proposed by Liang et al. is a faster and more accurate algorithm as compared to other online learning algorithms. However, besides the advantages of OS-ELM machine, the original OS-ELM algorithm also introced some issues; first, the improved OS-ELM algorithms need to be network structure adjustment to improve learning promance; second, OS-ELM algorithm learning with stability will affect its generalization ability. For such reasons, in this paper we propose a survey of OS-ELM algorithm with the development of history and the latest results of researching which can hopefully support researchers in the furture.