The DOI matches its Crossref record; 77 of the 77 references it cites were found in a registry or an index.
An Insight into Extreme Learning Machines: Random Neurons, Random Features and Kernels
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Citations
Cites
Cortes, C., & Vapnik, V. (1995) · Machine Learning · doi:10.1007/bf00994018 · Cited by 59,421
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Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986) · Nature · doi:10.1038/323533a0 · Cited by 31,892
Learning Internal Representations by Error Propagation
Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1985) · doi:10.21236/ada164453 · Cited by 16,172
Parallel Distributed Processing: Explorations in the Microstructure of Cognition: Foundations
Rumelhart, D. E., McClelland, J. L., & AU (1986) · doi:10.7551/mitpress/5236.001.0001 · Cited by 15,686
Extreme learning machine: Theory and applications
Huang, G.-B., Zhu, Q.-Y., & Siew, C.-K. (2006) · Neurocomputing · doi:10.1016/j.neucom.2005.12.126 · Cited by 13,285
Self-citationThe perceptron: A probabilistic model for information storage and organization in the brain.
Rosenblatt, F. (1958) · Psychological Review · doi:10.1037/h0042519 · Cited by 12,280
LIII. On lines and planes of closest fit to systems of points in space
Pearson, K. (1901) · The London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science · doi:10.1080/14786440109462720 · Cited by 11,934
Ridge regression: Biased estimation for nonorthogonal problems
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Least Squares Support Vector Machine Classifiers
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Orthonormal bases of compactly supported wavelets
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Cited by
Extreme Learning Machine for Multilayer Perceptron
Tang, J., Deng, C., & Huang, G.-B. (2016) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2015.2424995 · Cited by 1,451
Self-citationAdvanced Spectral Classifiers for Hyperspectral Images: A review
Ghamisi, P., Plaza, J., Chen, Y., Li, J., & Plaza, A. J. (2017) · IEEE Geoscience and Remote Sensing Magazine · doi:10.1109/mgrs.2016.2616418 · Cited by 675
A review on neural networks with random weights
Cao, W., Wang, X., Ming, Z., & Gao, J. (2018) · Neurocomputing · doi:10.1016/j.neucom.2017.08.040 · Cited by 466
High-Performance Extreme Learning Machines: A Complete Toolbox for Big Data Applications
Akusok, A., Bjork, K.-M., Miche, Y., & Lendasse, A. (2015) · IEEE Access · doi:10.1109/access.2015.2450498 · Cited by 269
Non-iterative and Fast Deep Learning: Multilayer Extreme Learning Machines
Zhang, J., Li, Y., Xiao, W., & Zhang, Z. (2020) · Journal of the Franklin Institute · doi:10.1016/j.jfranklin.2020.04.033 · Cited by 179
Metadata
- Authors
- Huang, G.-B.
- Venue
- Cognitive Computation
- Published
- 2014-04-03
- Volume
- 6
- Issue
- 3
- Pages
- 376-390
- ISSN
- 1866-9956 (ISSN-L)
- 1866-9964
- OpenAlex
- W2101674911
- Cited by
- 957
- References
- 77
- FWCI
- 110.91
- Type
- journal article
- Language
- en
- Open access
- closed
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- Huang, G.-B. (2014). An Insight into Extreme Learning Machines: Random Neurons, Random Features and Kernels. Cognitive Computation. https://doi.org/10.1007/s12559-014-9255-2
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
