The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Fuzzy support vector machines
Abstract
A support vector machine (SVM) learns the decision surface from two distinct classes of the input points. In many applications, each input point may not be fully assigned to one of these two classes. In this paper, we apply a fuzzy membership to each input point and reformulate the SVMs such that different input points can make different contributions to the learning of decision surface. We call the proposed method fuzzy SVMs (FSVMs).
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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
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Uncertainty in Big Data Analytics: Survey, Opportunities, and Challenges
Hariri, R. H., Fredericks, E. M., & Bowers, K. M. (2019) · Journal Of Big Data · doi:10.1186/s40537-019-0206-3 · Cited by 620
Machine learning and AI in marketing–Connecting computing power to human insights
Ma, L., & Sun, B. (2020) · International Journal of Research in Marketing · doi:10.1016/j.ijresmar.2020.04.005 · Cited by 602
Karatzoglou, A., Meyer, D., & Hornik, K. (2006) · Journal of Statistical Software · doi:10.18637/jss.v015.i09 · Cited by 592
Loyola-Gonzalez, O. (2019) · IEEE Access · doi:10.1109/access.2019.2949286 · Cited by 559
Metadata
- Authors
- Lin, C.-F., & Wang, S.-D.
- Venue
- IEEE Transactions on Neural Networks
- Published
- 2002-03
- Volume
- 13
- Issue
- 2
- Pages
- 464-471
- ISSN
- 1045-9227 (ISSN-L)
- OpenAlex
- W1994807229
- Cited by
- 1,494
- References
- 2
- FWCI
- 15.72
- Type
- journal article
- Language
- en
- Open access
- closed
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