The DOI matches its Crossref record; 57 of the 57 references it cites were found in a registry or an index.
A hybrid deep learning CNN–ELM for age and gender classification
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Citations
Cites
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
Gradient-based learning applied to document recognition
Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998) · Proceedings of the IEEE · doi:10.1109/5.726791 · Cited by 58,805
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
3D Convolutional Neural Networks for Human Action Recognition
Ji, S., Xu, W., Yang, M., & Yu, K. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2012.59 · Cited by 6,235
Face Description with Local Binary Patterns: Application to Face Recognition
Ahonen, T., Hadid, A., & Pietikainen, M. (2006) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2006.244 · Cited by 5,584
Extreme Learning Machine for Regression and Multiclass Classification
Huang, G.-B., Zhou, H., Ding, X., & Zhang, R. (2012) · IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) · doi:10.1109/tsmcb.2011.2168604 · Cited by 5,550
Caffe: Convolutional Architecture for Fast Feature Embedding
Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., & Darrell, T. J. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1408.5093 · Cited by 3,752
Face recognition: a convolutional neural-network approach
Lawrence, S., Giles, C. L., Tsoi, A. C., & Back, A. D. (1997) · IEEE Transactions on Neural Networks · doi:10.1109/72.554195 · Cited by 3,092
Universal Approximation using Incremental Constructive Feedforward Networks with Random Hidden Nodes
Huang, G.-B., Chen, L., & Siew, C.-K. (2006) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2006.875977 · Cited by 2,614
Introduction to multi-layer feed-forward neural networks
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Cited by
Younis, H. A., Ruhaiyem, N. I. R., Badr, A. A., Abdul-Hassan, A. K., Alfadli, I. M., Binjumah, W. M., Altuwaijri, E. A., & Nasser, M. (2023) · Processes · doi:10.3390/pr11051488 · Cited by 21
Metadata
- Authors
- Duan, M., Li, K., Yang, C., & Li, K.
- Venue
- Neurocomputing
- Publisher
- Elsevier BV
- Published
- 2018-01
- Volume
- 275
- Pages
- 448-461
- ISSN
- 0925-2312 (ISSN-L)
- OpenAlex
- W2751647243
- Cited by
- 258
- References
- 57
- FWCI
- 10.00
- Type
- journal article
- Language
- en
- Open access
- closed
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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- The paper
- Duan, M., Li, K., Yang, C., & Li, K. (2018). A hybrid deep learning CNN–ELM for age and gender classification. Neurocomputing. https://doi.org/10.1016/j.neucom.2017.08.062
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.
