A review on extreme learning machine

• Published in: Multimedia tools and applications Vol. 81; no. 29; pp. 41611 - 41660
• Main Authors: Wang, Jian, Lu, Siyuan, Wang, Shui-Hua, Zhang, Yu-Dong
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.12.2022; Springer Nature B.V
• Subjects: 1181: Multimedia-based Healthcare Systems using Computational Intelligence; Algorithms; Approximation; Artificial neural networks; Bias; Classification; Clustering; Cognitive tasks; Computed tomography; Computer Communication Networks; Computer Science; Data Structures and Information Theory; Machine learning; Medical imaging; Multimedia; Multimedia Information Systems; Neural networks; Neurons; Special Purpose and Application-Based Systems; optimization; extreme learning machine; regression; clustering; classification; neural network; medical imaging
• ISSN: 1380-7501; 1573-7721
• DOI: 10.1007/s11042-021-11007-7

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.