The DOI matches its Crossref record; 37 of the 37 references it cites were found in a registry or an index.
Extreme learning machine: algorithm, theory and applications
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Citations
Cites
A logical calculus of the ideas immanent in nervous activity
McCulloch, W. S., & Pitts, W. (1943) · The Bulletin of Mathematical Biophysics · doi:10.1007/bf02478259 · Cited by 18,566
Goldberg, D. E., & Shakespeare, W. (2002) · Cited by 16,088
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
Learning Deep Architectures for AI
Bengio, Y. (2009) · Foundations and Trends® in Machine Learning · doi:10.1561/2200000006 · Cited by 6,938
Approximation capabilities of multilayer feedforward networks
Hornik, K. (1991) · Neural Networks · doi:10.1016/0893-6080(91)90009-t · Cited by 6,091
Learning Deep Architectures for AI
Bengio, Y. (2009) · now publishers, Inc. eBooks · doi:10.1561/9781601982957 · Cited by 4,941
Convolutional networks for images, speech, and time series
LeCun, Y., & Bengio, Y. (1998) · HAL (Le Centre pour la Communication Scientifique Directe) · doi:10.5555/303568.303704 · Cited by 4,231
Extreme learning machine: a new learning scheme of feedforward neural networks
Huang, G.-B., Zhu, Q.-Y., & Siew, C.-K. (2005) · 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541) · doi:10.1109/ijcnn.2004.1380068 · Cited by 4,084
Leshno, M., Lin, V. Y., Pinkus, A., & Schocken, S. (1993) · Neural Networks · doi:10.1016/s0893-6080(05)80131-5 · Cited by 2,121
A Fast and Accurate Online Sequential Learning Algorithm for Feedforward Networks
Liang, N.-Y., Huang, G.-B., Saratchandran, P., & Sundararajan, N. (2006) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2006.880583 · Cited by 1,965
Cited by
A survey towards an integration of big data analytics to big insights for value-creation
Saggi, M. K., & Jain, S. (2018) · Information Processing & Management · doi:10.1016/j.ipm.2018.01.010 · Cited by 494
Resampling-Based Ensemble Methods for Online Class Imbalance Learning
Wang, S., Minku, L. L., & Yao, X. (2015) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2014.2345380 · Cited by 396
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
Wei, S., Chongchong, Z., & Cuiping, S. (2018) · Carbon Management · doi:10.1080/17583004.2018.1522095 · Cited by 66
Ren, Z., & Du, C. (2022) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2022.10.284 · Cited by 13
Metadata
- Authors
- Ding, S., Zhao, H., Zhang, Y., Xu, X., & Nie, R.
- Venue
- Artificial Intelligence Review
- Published
- 2013-04-23
- Volume
- 44
- Issue
- 1
- Pages
- 103-115
- ISSN
- 0269-2821 (ISSN-L)
- 1573-7462
- OpenAlex
- W1965895201
- Cited by
- 593
- References
- 37
- FWCI
- 13.62
- Type
- journal article
- Language
- en
- Open access
- closed
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- The paper
- Ding, S., Zhao, H., Zhang, Y., Xu, X., & Nie, R. (2015). Extreme learning machine: algorithm, theory and applications. Artificial Intelligence Review. https://doi.org/10.1007/s10462-013-9405-z
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.
