A Survey of Online Sequential Extreme Learning Machine
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.
2018 5th International Conference on Control, Decision and Information Technologies (CoDIT)