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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Published inMultimedia 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
LanguageEnglish
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
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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.
AbstractList 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.
Author Wang, Jian
Zhang, Yu-Dong
Lu, Siyuan
Wang, Shui-Hua
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  givenname: Jian
  surname: Wang
  fullname: Wang, Jian
  organization: School of Informatics, University of Leicester
– sequence: 2
  givenname: Siyuan
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  fullname: Lu, Siyuan
  organization: School of Informatics, University of Leicester
– sequence: 3
  givenname: Shui-Hua
  surname: Wang
  fullname: Wang, Shui-Hua
  email: shuihuawang@ieee.org
  organization: School of Computer Science and Technology, Henan Polytechnic University, Department of Cardiovascular Sciences, University of Leicester, Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, School of Architecture Building and Civil engineering, Loughborough University
– sequence: 4
  givenname: Yu-Dong
  orcidid: 0000-0002-4870-1493
  surname: Zhang
  fullname: Zhang, Yu-Dong
  email: yudongzhang@ieee.org
  organization: School of Informatics, University of Leicester, School of Computer Science and Technology, Henan Polytechnic University, Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University
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Keywords optimization
extreme learning machine
regression
clustering
classification
neural network
medical imaging
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Snippet Extreme learning machine (ELM) is a training algorithm for single hidden layer feedforward neural network (SLFN), which converges much faster than traditional...
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SubjectTerms 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
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Title A review on extreme learning machine
URI https://link.springer.com/article/10.1007/s11042-021-11007-7
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