The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
On the convergence of the decomposition method for support vector machines
Abstract
The decomposition method is currently one of the major methods for solving support vector machines (SVM). Its convergence properties have not been fully understood. The general asymptotic convergence was first proposed by Chang et al. However, their working set selection does not coincide with existing implementation. A later breakthrough by Keerthi and Gilbert (2000, 2002) proved the convergence finite termination for practical cases while the size of the working set is restricted to two. In this paper, we prove the asymptotic convergence of the algorithm used by the software SVM(light) and other later implementation. The size of the working set can be any even number. Extensions to other SVM formulations are also discussed.
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Citations
Cites
Cortes, C., & Vapnik, V. (1995) · Machine Learning · doi:10.1007/bf00994018 · Cited by 59,421
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-citationEstimating the Support of a High-Dimensional Distribution
Schölkopf, B., Platt, J. C., Shawe-Taylor, J., Smola, A. J., & Williamson, R. C. (2001) · Neural Computation · doi:10.1162/089976601750264965 · Cited by 6,168
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 tutorial on support vector regression
Smola, A. J., & Schölkopf, B. (2004) · Statistics and Computing · doi:10.1023/b:stco.0000035301.49549.88 · Cited by 13,387
(1999) · doi:10.1162/089976600300015565 · Cited by 2,799
Incremental support vector learning for ordinal regression
Gu, B., Sheng, V. S., Tay, K. Y., Romano, W., & Li, S. (2015) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2014.2342533 · Cited by 707
A Study on SMO-Type Decomposition Methods for Support Vector Machines
Chen, P.-H., Fan, R.-E., & Lin, C.-J. (2006) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2006.875973 · Cited by 339
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Metadata
- Authors
- Lin, C.-J.
- Venue
- IEEE Transactions on Neural Networks
- Published
- 2001
- Volume
- 12
- Issue
- 6
- Pages
- 1288-1298
- ISSN
- 1045-9227 (ISSN-L)
- OpenAlex
- W2047542122
- Cited by
- 245
- References
- 3
- FWCI
- 13.89
- Type
- journal article
- Language
- en
- Open access
- closed
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