A review on extreme learning machine

被引：0
作者：
Jian Wang
Siyuan Lu
Shui-Hua Wang
Yu-Dong Zhang
机构：
[1] University of Leicester,School of Informatics
[2] Henan Polytechnic University,School of Computer Science and Technology
[3] University of Leicester,Department of Cardiovascular Sciences
[4] King Abdulaziz University,Department of Information Systems, Faculty of Computing and Information Technology
[5] Loughborough University,School of Architecture Building and Civil engineering
来源：
Multimedia Tools and Applications | 2022年 / 81卷
关键词：
extreme learning machine; neural network; medical imaging; classification; optimization; clustering; regression;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
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.
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页码：41611 / 41660
页数：49
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