The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
A fast iterative nearest point algorithm for support vector machine classifier design
Abstract
In this paper we give a new fast iterative algorithm for support vector machine (SVM) classifier design. The basic problem treated is one that does not allow classification violations. The problem is converted to a problem of computing the nearest point between two convex polytopes. The suitability of two classical nearest point algorithms, due to Gilbert, and Mitchell et al., is studied. Ideas from both these algorithms are combined and modified to derive our fast algorithm. For problems which require classification violations to be allowed, the violations are quadratically penalized and an idea due to Cortes and Vapnik and Friess is used to convert it to a problem in which there are no classification violations. Comparative computational evaluation of our algorithm against powerful SVM methods such as Platt's sequential minimal optimization shows that our algorithm is very competitive.
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Cites
Cortes, C., & Vapnik, V. (1995) · Machine Learning · doi:10.1007/bf00994018 · Cited by 59,421
The Nature of Statistical Learning Theory
Vapnik, V. N. (1995) · doi:10.1007/978-1-4757-2440-0 · Cited by 39,138
Solving Least Squares Problems
Lawson, C. L., & Hanson, R. J. (1995) · doi:10.1137/1.9781611971217 · Cited by 7,103
An Iterative Procedure for Computing the Minimum of a Quadratic Form on a Convex Set
Gilbert, E. G. (1966) · SIAM Journal on Control · doi:10.1137/0304007 · Cited by 230
Cited by
Support vector machine in machine condition monitoring and fault diagnosis
Widodo, A., & Yang, B.-S. (2007) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2006.12.007 · Cited by 1,650
SVM-KM: speeding SVMs learning with a priori cluster selection and k-means
Barros de Almeida, M., de Padua Braga, A., & Braga, J. P. (2002) · Proceedings. Vol.1. Sixth Brazilian Symposium on Neural Networks · doi:10.1109/sbrn.2000.889732 · Cited by 130
Decomposition Methods for Linear Support Vector Machines
Kao, W.-C., Chung, K.-M., Sun, C.-L., & Lin, C.-J. (2004) · Neural Computation · doi:10.1162/089976604774201640 · Cited by 54
An Equivalence between the Lasso and Support Vector Machines
Jaggi, M. (2013) · arXiv · doi:10.48550/arxiv.1303.1152 · Cited by 35
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Metadata
- Authors
- Keerthi, S. S., Shevade, S. K., Bhattacharyya, C., & Murthy, K. R. K.
- Venue
- IEEE Transactions on Neural Networks
- Published
- 2000
- Volume
- 11
- Issue
- 1
- Pages
- 124-136
- ISSN
- 1045-9227 (ISSN-L)
- OpenAlex
- W2166282318
- Cited by
- 383
- References
- 4
- FWCI
- 22.07
- Type
- journal article
- Language
- en
- Open access
- closed
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- Keerthi, S. S., Shevade, S. K., Bhattacharyya, C., & Murthy, K. R. K. (2000). A fast iterative nearest point algorithm for support vector machine classifier design. IEEE Transactions on Neural Networks. https://doi.org/10.1109/72.822516
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