A review on extreme learning machine

Jian Wang, Siyuan Lu, Shui Hua Wang*, Yu Dong Zhang*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

181 Citations (Scopus)

Abstract

Extreme learning machine (ELM) is a training algorithm for single hidden layer feedforward neural network (SLFN), which converges much faster than traditional methods and yields promising performance. In this paper, we hope to present a comprehensive review on ELM. Firstly, we will focus on the theoretical analysis including universal approximation theory and generalization. Then, the various improvements are listed, which help ELM works better in terms of stability, efficiency, and accuracy. Because of its outstanding performance, ELM has been successfully applied in many real-time learning tasks for classification, clustering, and regression. Besides, we report the applications of ELM in medical imaging: MRI, CT, and mammogram. The controversies of ELM were also discussed in this paper. We aim to report these advances and find some future perspectives.

Original languageEnglish
Pages (from-to)41611-41660
Number of pages50
JournalMultimedia Tools and Applications
Volume81
Issue number29
DOIs
Publication statusPublished - Dec 2022
Externally publishedYes

Keywords

  • classification
  • clustering
  • extreme learning machine
  • medical imaging
  • neural network
  • optimization
  • regression

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