The DOI matches its Crossref record; 68 of the 68 references it cites were found in a registry or an index.
A review of optimization methodologies in support vector machines
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Boyd, S., & Vandenberghe, L. (2004) · doi:10.1017/cbo9780511804441 · Cited by 56,601
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
The Nature of Statistical Learning Theory
Vapnik, V. N. (1995) · doi:10.1007/978-1-4757-2440-0 · Cited by 39,138
(2021) · A First Course in Statistical Programming with R · doi:10.1017/9781108993456.011 · Cited by 13,724
A training algorithm for optimal margin classifiers
Boser, B. E., Guyon, I. M., & Vapnik, V. N. (1992) · Proceedings of the fifth annual workshop on Computational learning theory · doi:10.1145/130385.130401 · Cited by 11,992
Gaussian Processes for Machine Learning
Rasmussen, C. E., & Williams, C. K. I. (2005) · doi:10.7551/mitpress/3206.001.0001 · Cited by 11,705
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Schölkopf, B., & Smola, A. J. (2001) · The MIT Press eBooks · doi:10.7551/mitpress/4175.001.0001 · Cited by 11,703
Proceedings of the 24th international conference on Machine learning
(2007) · doi:10.1145/1273496 · Cited by 11,630
(1999) · Springer Series in Operations Research and Financial Engineering · doi:10.1007/b98874 · Cited by 9,482
Kernel Methods for Pattern Analysis
Shawe-Taylor, J., & Cristianini, N. (2004) · Cambridge University Press eBooks · doi:10.1017/cbo9780511809682 · Cited by 6,587
Self-citation
Cited by
Mathematics for Machine Learning
Deisenroth, M. P., Faisal, A. A., & Ong, C. S. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108679930 · Cited by 573
Antropova, N., Huynh, B. Q., & Giger, M. L. (2017) · Medical Physics · doi:10.1002/mp.12453 · Cited by 389
A review of recent advances and strategies in transfer learning
Gholizade, M., Soltanizadeh, H., Rahmanimanesh, M., & Sana, S. S. (2025) · International Journal of System Assurance Engineering and Management · doi:10.1007/s13198-024-02684-2 · Cited by 92
Kamel, S. R., YaghoubZadeh, R., & Kheirabadi, M. (2019) · Journal of Big Data · doi:10.1186/s40537-019-0247-7 · Cited by 50
Quantum computing and supervised machine learning
Oneto, L., Ridella, S., & Anguita, D. (2017) · Quantum Inspired Computational Intelligence · doi:10.1016/b978-0-12-804409-4.00002-4 · Cited by 11
Metadata
- Authors
- Shawe-Taylor, J., & Sun, S.
- Venue
- Neurocomputing
- Publisher
- Elsevier BV
- Published
- 2011-10
- Volume
- 74
- Issue
- 17
- Pages
- 3609-3618
- ISSN
- 0925-2312 (ISSN-L)
- OpenAlex
- W2123367805
- Cited by
- 256
- References
- 68
- FWCI
- 13.90
- Type
- journal article
- Language
- en
- Open access
- closed
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- Shawe-Taylor, J., & Sun, S. (2011). A review of optimization methodologies in support vector machines. Neurocomputing. https://doi.org/10.1016/j.neucom.2011.06.026
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.
