Extreme learning machine: algorithm, theory and applications
Extreme learning machine (ELM) is a new learning algorithm for the single hidden layer feedforward neural networks. Compared with the conventional neural network learning algorithm it overcomes the slow training speed and over-fitting problems. ELM is based on empirical risk minimization theory and...
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| Veröffentlicht in: | The Artificial intelligence review Jg. 44; H. 1; S. 103 - 115 |
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| Hauptverfasser: | , , , , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
Dordrecht
Springer Netherlands
01.06.2015
Springer Springer Nature B.V |
| Schlagworte: | |
| ISSN: | 0269-2821, 1573-7462 |
| Online-Zugang: | Volltext |
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| Abstract | Extreme learning machine (ELM) is a new learning algorithm for the single hidden layer feedforward neural networks. Compared with the conventional neural network learning algorithm it overcomes the slow training speed and over-fitting problems. ELM is based on empirical risk minimization theory and its learning process needs only a single iteration. The algorithm avoids multiple iterations and local minimization. It has been used in various fields and applications because of better generalization ability, robustness, and controllability and fast learning rate. In this paper, we make a review of ELM latest research progress about the algorithms, theory and applications. It first analyzes the theory and the algorithm ideas of ELM, then tracking describes the latest progress of ELM in recent years, including the model and specific applications of ELM, finally points out the research and development prospects of ELM in the future. |
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| AbstractList | Extreme learning machine (ELM) is a new learning algorithm for the single hidden layer feedforward neural networks. Compared with the conventional neural network learning algorithm it overcomes the slow training speed and over-fitting problems. ELM is based on empirical risk minimization theory and its learning process needs only a single iteration. The algorithm avoids multiple iterations and local minimization. It has been used in various fields and applications because of better generalization ability, robustness, and controllability and fast learning rate. In this paper, we make a review of ELM latest research progress about the algorithms, theory and applications. It first analyzes the theory and the algorithm ideas of ELM, then tracking describes the latest progress of ELM in recent years, including the model and specific applications of ELM, finally points out the research and development prospects of ELM in the future. |
| Audience | Academic |
| Author | Zhang, Yanan Xu, Xinzheng Ding, Shifei Zhao, Han Nie, Ru |
| Author_xml | – sequence: 1 givenname: Shifei surname: Ding fullname: Ding, Shifei email: dingsf@cumt.edu.cn organization: School of Computer Science and Technology, China University of Mining and Technology, Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Science – sequence: 2 givenname: Han surname: Zhao fullname: Zhao, Han organization: School of Computer Science and Technology, China University of Mining and Technology – sequence: 3 givenname: Yanan surname: Zhang fullname: Zhang, Yanan organization: School of Computer Science and Technology, China University of Mining and Technology – sequence: 4 givenname: Xinzheng surname: Xu fullname: Xu, Xinzheng organization: School of Computer Science and Technology, China University of Mining and Technology – sequence: 5 givenname: Ru surname: Nie fullname: Nie, Ru organization: School of Computer Science and Technology, China University of Mining and Technology |
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| Keywords | Extreme learning machine (ELM) Local minimum Over-fitting Single-hidden layer feedforward neural networks (SLFNs) Least-squares |
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| SubjectTerms | Algorithms Application Artificial Intelligence Artificial neural networks Computer Science Data mining Information processing Learning Machine learning Minimization Neural networks Optimization Pattern recognition R&D Research & development Robot learning Robust control Robustness Theory Tracking |
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