The DOI matches its Crossref record; 28 of the 28 references it cites were found in a registry or an index.
A Study on SMO-Type Decomposition Methods for Support Vector Machines
Abstract
Decomposition methods are currently one of the major methods for training support vector machines. They vary mainly according to different working set selections. Existing implementations and analysis usually consider some specific selection rules. This paper studies sequential minimal optimization type decomposition methods under a general and flexible way of choosing the two-element working set. The main results include: 1) a simple asymptotic convergence proof, 2) a general explanation of the shrinking and caching techniques, and 3) the linear convergence of the methods. Extensions to some support vector machine variants are also discussed.
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Cites
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Cited by
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
Self-citationA comprehensive survey on support vector machine classification: Applications, challenges and trends
Cervantes, J., Garcia-Lamont, F., Rodríguez-Mazahua, L., & Lopez, A. (2020) · Neurocomputing · doi:10.1016/j.neucom.2019.10.118 · Cited by 2,381
Support Vector Machine Solvers
Bottou, L., & Lin, C.-J. (2007) · Large-Scale Kernel Machines · doi:10.7551/mitpress/7496.003.0003 · Cited by 336
Self-citationRandomized Sampling for Large Data Applications of SVM
Ferragut, E. M., & Laska, J. (2012) · 2012 11th International Conference on Machine Learning and Applications · doi:10.1109/icmla.2012.65 · Cited by 16
Metadata
- Authors
- Chen, P.-H., Fan, R.-E., & Lin, C.-J.
- Venue
- IEEE Transactions on Neural Networks
- Published
- 2006-07
- Volume
- 17
- Issue
- 4
- Pages
- 893-908
- ISSN
- 1045-9227 (ISSN-L)
- 1941-0093
- OpenAlex
- W2141057577
- Cited by
- 339
- References
- 28
- FWCI
- 24.88
- Type
- journal article
- Language
- en
- Open access
- closed
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