Extreme learning machines: new trends and applications
Article Properties
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LanguageEnglish
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DOI (url)
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Publication Date2015/01/13
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Indian UGC (journal)
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Refrences61
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Citations60
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ChenWei Deng
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GuangBin Huang
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Jia Xu
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JieXiong Tang
Cite
Deng, ChenWei, et al. “Extreme Learning Machines: New Trends and Applications”. Science China Information Sciences, vol. 58, no. 2, 2015, pp. 1-16, https://doi.org/10.1007/s11432-014-5269-3.
Deng, C., Huang, G., Xu, J., & Tang, J. (2015). Extreme learning machines: new trends and applications. Science China Information Sciences, 58(2), 1-16. https://doi.org/10.1007/s11432-014-5269-3
Deng, ChenWei, GuangBin Huang, Jia Xu, and JieXiong Tang. “Extreme Learning Machines: New Trends and Applications”. Science China Information Sciences 58, no. 2 (2015): 1-16. https://doi.org/10.1007/s11432-014-5269-3.
Deng C, Huang G, Xu J, Tang J. Extreme learning machines: new trends and applications. Science China Information Sciences. 2015;58(2):1-16.
Journal Categories
Science
Mathematics
Instruments and machines
Electronic computers
Computer science
Science
Mathematics
Instruments and machines
Electronic computers
Computer science
Computer software
Science
Science (General)
Cybernetics
Information theory
Technology
Electrical engineering
Electronics
Nuclear engineering
Electric apparatus and materials
Electric circuits
Electric networks
Technology
Electrical engineering
Electronics
Nuclear engineering
Electronics
Computer engineering
Computer hardware
Refrences
| Title | Journal | Journal Categories | Citations | Publication Date |
|---|---|---|---|---|
| An Insight into Extreme Learning Machines: Random Neurons, Random Features and Kernels | Cognitive Computation |
| 590 | 2014 |
| Circular-ELM for the reduced-reference assessment of perceived image quality | Neurocomputing |
| 40 | 2013 |
| 10.1109/TPAMI.2013.50 | 2013 | |||
| 10.1109/MIS.2013.4 | IEEE Intelligent Systems |
| 2013 | |
| 10.1109/TIP.2012.2214048 | IEEE Transactions on Image Processing |
| 2013 |
Refrences Analysis
The category
Science: Mathematics: Instruments and machines: Electronic computers. Computer science 28
is the most frequently represented among the references in this article. It primarily includes studies from IEEE Transactions on Image Processing and Neurocomputing. The chart below illustrates the number of referenced publications per year.
Refrences used by this article by year
Citations
| Title | Journal | Journal Categories | Citations | Publication Date |
|---|---|---|---|---|
| Grey wolf optimization-extreme learning machine for automatic spoken language identification | Multimedia Tools and Applications |
| 5 | 2023 |
| Multilayer extreme learning machine: a systematic review | Multimedia Tools and Applications |
| 1 | 2023 |
| A RUL prediction method for lithium-ion batteries based on improved singular spectrum analysis and CSA-KELM | Microelectronics Reliability |
| 2 | 2023 |
Two-Direction Prediction Method of Drilling Fluid Based on OS-ELM for Water Well Drilling | Journal of Advanced Computational Intelligence and Intelligent Informatics |
| 2023 | |
A Gradient-Based Particle-Bat Algorithm for Stochastic Configuration Network | Applied Sciences |
| 1 | 2023 |
Citations Analysis
The category
Science: Mathematics: Instruments and machines: Electronic computers. Computer science 25
is the most commonly referenced area in studies that cite this article. The first research to cite this article was titled Bio-inspired neural network with application to license plate recognition: hysteretic ELM approach and was published in 2016. The most recent citation comes from a 2023 study titled Two-Direction Prediction Method of Drilling Fluid Based on OS-ELM for Water Well Drilling. This article reached its peak citation in 2021, with 12 citations. It has been cited in 47 different journals, 25% of which are open access. Among related journals, the Neurocomputing cited this research the most, with 6 citations. The chart below illustrates the annual citation trends for this article.