Journal article
Spline function smooth support vector machine for classification
Journal of industrial and management optimization, Vol.3(3), pp.529-542
2007
DOI: 10.3934/jimo.2007.3.529
Abstract
Support vector machine (SVM) is a very popular method for binary data classification in data mining (machine learning). Since the objective function of the unconstrained SVM model is a non-smooth function, a lot of good optimal algorithms can't be used to find the solution. In order to overcome this model's non-smooth property, Lee and Mangasarian proposed smooth support vector machine (SSVM) in 2001. Later, Yuan et al. proposed the polynomial smooth support vector machine (PSSVM) in 2005. In this paper, a three-order spline function is used to smooth the objective function and a three-order spline smooth support vector machine model (TSSVM) is obtained. By analyzing the performance of the smooth function, the smooth precision has been improved obviously. Moreover, BFGS and Newton-Armijo algorithms are used to solve the TSSVM model. Our experimental results prove that the TSSVM model has better classification performance than other competitive baselines. [PUBLICATION ABSTRACT]
Details
- Title: Subtitle
- Spline function smooth support vector machine for classification
- Creators
- Yubo YuanWeiguo FanDongmei Pu
- Resource Type
- Journal article
- Publication Details
- Journal of industrial and management optimization, Vol.3(3), pp.529-542
- Publisher
- American Institute of Mathematical Sciences
- DOI
- 10.3934/jimo.2007.3.529
- ISSN
- 1547-5816
- eISSN
- 1553-166X
- Language
- English
- Date published
- 2007
- Academic Unit
- Business Analytics
- Record Identifier
- 9984380456202771
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