A review on extreme learning machine

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 theo...

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Bibliographic Details
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
Online Access:Get full text
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Summary: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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ISSN:1380-7501
1573-7721
DOI:10.1007/s11042-021-11007-7