The DOI matches its Crossref record; 29 of the 29 references it cites were found in a registry or an index.
A study on effectiveness of extreme learning machine
Open accessAbstract
Abstract Extreme learning machine (ELM), proposed by Huang et al., has been shown a promising learning algorithm for single-hidden layer feedforward neural networks (SLFNs). Nevertheless, because of the random choice of input weights and biases, the ELM algorithm sometimes makes the hidden layer output matrix H of SLFN not full column rank, which lowers the effectiveness of ELM. This paper discusses the effectiveness of ELM and proposes an improved algorithm called EELM that makes a proper selection of the input weights and bias before calculating the output weights, which ensures the full column rank of H in theory. This improves to some extend the learning rate (testing accuracy, prediction accuracy, learning time) and the robustness property of the networks. The experimental results based on both the benchmark function approximation and real-world problems including classification and regression applications show the good performances of EELM.
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Cited by
Extreme learning machines: a survey
Huang, G.-B., Wang, D. H., & Lan, Y. (2011) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-011-0019-y · Cited by 1,914
Extreme learning machine: algorithm, theory and applications
Ding, S., Zhao, H., Zhang, Y., Xu, X., & Nie, R. (2015) · Artificial Intelligence Review · doi:10.1007/s10462-013-9405-z · Cited by 593
A review on extreme learning machine
Wang, J., Lu, S., Wang, S.-H., & Zhang, Y.-D. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-021-11007-7 · Cited by 572
Exploring ELM-based spatial–spectral classification of hyperspectral images
Heras, D. B., Argüello, F., & Quesada-Barriuso, P. (2014) · International Journal of Remote Sensing · doi:10.1080/01431161.2013.869633 · Cited by 54
Argüello, F., & Heras, D. B. (2015) · International Journal of Remote Sensing · doi:10.1080/01431161.2014.999882 · Cited by 27
Metadata
- Authors
- Wang, Y., Cao, F., & Yuan, Y.
- Venue
- Neurocomputing
- Publisher
- Elsevier BV
- Published
- 2011-09
- Volume
- 74
- Issue
- 16
- Pages
- 2483-2490
- ISSN
- 0925-2312 (ISSN-L)
- OpenAlex
- W2028759731
- Cited by
- 269
- References
- 29
- FWCI
- 17.44
- Type
- journal article
- Language
- en
- Open access
- green
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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