The DOI matches its Crossref record; 143 of the 143 references it cites were found in a registry or an index.
A comprehensive survey on regularization strategies in machine learning
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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
Regression shrinkage and selection via the lasso
Tibshirani, R. (1996) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1996.tb02080.x · Cited by 51,482
(2014) · Cited by 33,419
SMOTE synthetic minority over-sampling technique
Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002) · Journal of Artificial Intelligence Research · doi:10.1613/jair.953 · Cited by 32,865
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Ioffe, S., & Szegedy, C. (2015) · arXiv · doi:10.48550/arxiv.1502.03167 · Cited by 27,818
A fast learning algorithm for deep belief nets
Hinton, G. E., Osindero, S., & Teh, Y.-W. (2006) · Neural Computation · doi:10.1162/neco.2006.18.7.1527 · Cited by 16,336
Visualizing and understanding convolutional networks
Zeiler, M. D., & Fergus, R. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10590-1_53 · Cited by 15,839
Advances in neural information processing systems 7
(1996) · Computers & Mathematics with Applications · doi:10.1016/s0898-1221(96)90463-0 · Cited by 14,281
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Ioffe, S. (2024) · arXiv (Cornell University) · doi:10.57702/o9raffed · Cited by 14,214
A survey on Image Data Augmentation for Deep Learning
Shorten, C., & Khoshgoftaar, T. M. (2019) · Journal of Big Data · doi:10.1186/s40537-019-0197-0 · Cited by 13,203
Cited by
Lekadir, K., Frangi, A. F., Porras, A. R., Glocker, B., Cintas, C., Langlotz, C. P., Weicken, E., Asselbergs, F. W., Prior, F., Collins, G. S., et al. (2025) · BMJ · doi:10.1136/bmj-2024-081554 · Cited by 957
“Additionally, it is important to show that the AI tool is safe and does not cause harm to individuals (or specific groups), such as through a randomised clinical trial.”
Tripathy, K. P., & Mishra, A. K. (2024) · Journal of Hydrology · doi:10.1016/j.jhydrol.2023.130458 · Cited by 336
Domingos, L. C. F., Santos, P. E., Skelton, P. S. M., Brinkworth, R. S. A., & Sammut, K. (2022) · Sensors · doi:10.3390/s22062181 · Cited by 93
“Regularisation methods have received great attention from recent studies [61], since they are solely related to the algorithms, and not to data quality or classifier competitions.”
MethodShobayo, O., & Saatchi, R. (2025) · Diagnostics · doi:10.3390/diagnostics15091072 · Cited by 53
Artificial Intelligence to Improve Antibiotic Prescribing: A Systematic Review
Amin, D., Garzόn-Orjuela, N., Garcia Pereira, A., Parveen, S., Vornhagen, H., & Vellinga, A. (2023) · Antibiotics · doi:10.3390/antibiotics12081293 · Cited by 34
Metadata
- Authors
- Tian, Y., & Zhang, Y.
- Venue
- Information Fusion
- Publisher
- Elsevier BV
- Published
- 2022-04
- Volume
- 80
- Pages
- 146-166
- ISSN
- 1566-2535 (ISSN-L)
- OpenAlex
- W3213822864
- Cited by
- 298
- References
- 143
- FWCI
- 13.06
- Type
- journal article
- Language
- en
- Open access
- closed
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- Tian, Y., & Zhang, Y. (2022). A comprehensive survey on regularization strategies in machine learning. Information Fusion. https://doi.org/10.1016/j.inffus.2021.11.005
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.
