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- Publisher
- Springer Journals
- Copyright
- Copyright © 2018 by Classification Society of North America
- Subject
- Statistics; Statistical Theory and Methods; Pattern Recognition; Bioinformatics; Signal,Image and Speech Processing; Psychometrics; Marketing
- ISSN
- 0176-4268
- eISSN
- 1432-1343
- DOI
- 10.1007/s00357-018-9249-y
- Publisher site
- See Article on Publisher Site
Abstract
The hinge loss support vector machine (SVM) is sensitive to outliers. This paper proposes a new support vector machine with a pinball loss function (PSVM+). The new model is less sensitive to noise, especially the feature noise around the decision boundary. Furthermore, the PSVM+ is more stable than the hinge loss support vector machine plus (SVM+) for re-sampling. It also embeds the additional information into the corresponding optimization problem, which is helpful to further improve the learning performance. Meanwhile, the computational complexity of the PSVM+ is similar to that of the SVM+.
Journal
Journal of Classification – Springer Journals
Published: Mar 16, 2018