The DOI matches its Crossref record; 32 of the 32 references it cites were found in a registry or an index.
Support Vector Machine Classifier With Pinball Loss
Open accessAbstract
Traditionally, the hinge loss is used to construct support vector machine (SVM) classifiers. The hinge loss is related to the shortest distance between sets and the corresponding classifier is hence sensitive to noise and unstable for re-sampling. In contrast, the pinball loss is related to the quantile distance and the result is less sensitive. The pinball loss has been deeply studied and widely applied in regression but it has not been used for classification. In this paper, we propose a SVM classifier with the pinball loss, called pin-SVM, and investigate its properties, including noise insensitivity, robustness, and misclassification error. Besides, insensitive zone is applied to the pin-SVM for a sparse model. Compared to the SVM with the hinge loss, the proposed pin-SVM has the same computational complexity and enjoys noise insensitivity and re-sampling stability.
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Self-citationConvexity, Classification, and Risk Bounds
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Cited by
Gong, R., Wu, C., & Chu, M. (2018) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/j.chemolab.2017.11.018 · Cited by 44
A multi-class classifier based on support vector hyper-spheres for steel plate surface defects
Gong, R., Chu, M., Yang, Y., & Feng, Y. (2019) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/j.chemolab.2019.03.010 · Cited by 31
Metadata
- Authors
- Huang, X., Shi, L., & Suykens, J. A. K.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2014-05
- Volume
- 36
- Issue
- 5
- Pages
- 984-997
- ISSN
- 0162-8828 (ISSN-L)
- 2160-9292
- OpenAlex
- W2136627475
- Cited by
- 355
- References
- 32
- FWCI
- 5.89
- Type
- journal article
- Language
- en
- Open access
- green
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