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Parallel Sequential Minimal Optimization for the Training of Support Vector Machines
Abstract
Sequential minimal optimization (SMO) is one popular algorithm for training support vector machine (SVM), but it still requires a large amount of computation time for solving large size problems. This paper proposes one parallel implementation of SMO for training SVM. The parallel SMO is developed using message passing interface (MPI). Specifically, the parallel SMO first partitions the entire training data set into smaller subsets and then simultaneously runs multiple CPU processors to deal with each of the partitioned data sets. Experiments show that there is great speedup on the adult data set and the Mixing National Institute of Standard and Technology (MNIST) data set when many processors are used. There are also satisfactory results on the Web data set.
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Citations
Cited by
Improved Computation for Levenberg–Marquardt Training
Wilamowski, B. M., & Yu, H. (2010) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2010.2045657 · Cited by 622
Support Vector Machines for Classification
Awad, M., & Khanna, R. (2015) · Efficient Learning Machines · doi:10.1007/978-1-4302-5990-9_3 · Cited by 543
Fast support vector machine training and classification on graphics processors
Catanzaro, B., Sundaram, N., & Keutzer, K. (2008) · Proceedings of the 25th international conference on Machine learning - ICML '08 · doi:10.1145/1390156.1390170 · Cited by 372
Metadata
- Authors
- Cao, L. J., Keerthi, S. S., Ong, C.-J., Zhang, J. Q., Periyathamby, U., Fu, X. J., & Lee, H. P.
- Venue
- IEEE Transactions on Neural Networks
- Published
- 2006-07
- Volume
- 17
- Issue
- 4
- Pages
- 1039-1049
- ISSN
- 1045-9227 (ISSN-L)
- OpenAlex
- W2116173306
- Cited by
- 182
- References
- 0
- FWCI
- 9.08
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Parallel Sequential Minimal Optimization for the Training of Support Vector Machines. https://app.cochat.ai/papers/10.1109/tnn.2006.875989
- The paper
- Cao, L. J., Keerthi, S. S., Ong, C.-J., Zhang, J. Q., Periyathamby, U., Fu, X. J., & Lee, H. P. (2006). Parallel Sequential Minimal Optimization for the Training of Support Vector Machines. IEEE Transactions on Neural Networks. https://doi.org/10.1109/tnn.2006.875989
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