A comprehensive survey on support vector machine classification: Applications, challenges and trends
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Gao et al.'s approach was implemented using telemetry data from an actual in-orbit satellite and simulated by Matlab/Simulink.
“Their approach was implemented using telemetry data from an actual in-orbit satellite and simulated by Matlab/Simulink [136] [128].”
From Applications and Challenges of Artificial Intelligence in Space Missions (2024) · cites this paper as [136]
Cites
LIBSVM: A library for support vector machines
Chang, C.-C., & Lin, C.-J. (2011) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/1961189.1961199 · Cited by 40,918 · see citing works
Deep learning-based classification of hyperspectral data
Chen, Y., Lin, Z., Zhao, X., Wang, G., & Gu, Y. (2014) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2014.2329330 · Cited by 2,679 · see citing works
Data mining for credit card fraud: a comparative study
Bhattacharyya, S., Jha, S., Tharakunnel, K., & Westland, J. C. (2011) · Decision Support Systems · doi:10.1016/j.dss.2010.08.008 · Cited by 1,108 · see citing works
Deep learning for robust feature generation in audiovisual emotion recognition
Kim, Y., Lee, H., & Provost, E. M. (2013) · 2013 IEEE International Conference on Acoustics, Speech and Signal Processing · doi:10.1109/icassp.2013.6638346 · Cited by 401 · see citing works
KBA: kernel boundary alignment considering imbalanced data distribution
Wu, G., & Chang, E. Y. (2005) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2005.95 · Cited by 380 · see citing works
An efficient instance selection algorithm to reconstruct training set for support vector machine
Liu, C., Wang, W., Wang, M., Lv, F., & Konan, M. (2017) · Knowledge-Based Systems · doi:10.1016/j.knosys.2016.10.031 · Cited by 146 · see citing works
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Abavisani, M., Khoshrou, A., Foroushan, S. K., Ebadpour, N., & Sahebkar, A. (2024) · Current Research in Biotechnology · doi:10.1016/j.crbiot.2024.100211 · Cited by 75 · see citing works
Applications and Challenges of Artificial Intelligence in Space Missions
Oche, P. A., Ewa, G. A., & Ibekwe, N. (2024) · IEEE Access · doi:10.1109/access.2021.3132500 · Cited by 68 · see citing works
“However, for large data sets the training kernel matrix grows in quadratic form with the size of the data set, thus making the training of SVM on large data sets a very slow process [135].”
BackgroundInfluentialRostam, N. A. P., Malim, N. H. A. H., Abdullah, R., Ahmad, A. L., Ooi, B. S., & Chan, D. J. C. (2021) · IEEE Access · doi:10.1109/access.2021.3102044 · Cited by 55 · see citing works
Kanyongo, W., Ezugwu, A. E., Moyo, T., & Fonou Dombeu, J. V. (2025) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2024.101611 · Cited by 7 · see citing works
Falkovich, R., Aryal, S., Sheng, M., & Bathe, M. (2024) · doi:10.1101/2024.10.28.620504 · Cited by 2 · see citing works
- Authors
- Cervantes, J., Garcia-Lamont, F., Rodríguez-Mazahua, L., & Lopez, A.
- Affiliations
- Universidad Autónoma del Estado de México (MX)
- Instituto Tecnologico de Orizaba (MX)
- Corresponding author
- Cervantes, Jair
- Venue
- Neurocomputing
- Publisher
- Elsevier BV
- Published
- 2020-09
- Volume
- 408
- Pages
- 189-215
- ISSN
- 0925-2312 (ISSN-L)
- OpenAlex
- W3023211159
- Cited by
- 2,381
- References
- 6
- Citations by year
- 2026: 301 · 2025: 584 · 2024: 619 · 2023: 409 · 2022: 299 · 2021: 154 · 2020: 12 · 2019: 1
- FWCI
- 85.90
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Neurocomputing (publishedVersion)
- Institutional Repository (Universidad Autónoma del Estado de México) (submittedVersion, cc-by-nc-nd)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and ELM
- Physical Sciences › Computer Science › Information Systems › Data Mining and Machine Learning Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Algorithms
- Keywords
- support vector machine, classification, pattern recognition, large data sets, multi-class classification, class imbalance, evolutionary algorithms, hyperparameter optimization
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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.
Statistics and arithmetic are recomputed, not judged.
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