The DOI matches its Crossref record; 192 of the 192 references it cites were found in a registry or an index.
Optimization problems for machine learning: A survey
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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
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Scikit-learn: Machine Learning in Python
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The Nature of Statistical Learning Theory
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Cited by
Sustainable energies and machine learning: An organized review of recent applications and challenges
Ifaei, P., Nazari-Heris, M., Tayerani Charmchi, A. S., Asadi, S., & Yoo, C. (2023) · Energy · doi:10.1016/j.energy.2022.126432 · Cited by 79
AI-driven design optimization for sustainable buildings: a systematic review
Manmatharasan, P., Bitsuamlak, G., & Grolinger, K. (2025) · Energy and Buildings · doi:10.1016/j.enbuild.2025.115440 · Cited by 63
Metadata
- Authors
- Gambella, C., Ghaddar, B., & Naoum-Sawaya, J.
- Venue
- European Journal of Operational Research
- Publisher
- Elsevier BV
- Published
- 2021-05
- Volume
- 290
- Issue
- 3
- Pages
- 807-828
- ISSN
- 0377-2217 (ISSN-L)
- OpenAlex
- W2909324897
- Cited by
- 365
- References
- 192
- FWCI
- 14.02
- Type
- journal article
- Language
- en
- Open access
- green
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- Gambella, C., Ghaddar, B., & Naoum-Sawaya, J. (2021). Optimization problems for machine learning: A survey. European Journal of Operational Research. https://doi.org/10.1016/j.ejor.2020.08.045
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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.
