Construction and Practice of the Optimal Smooth Semi-Supervised Support Vector Machine

Zhang Xiaodan (), Li Ang () and Ran Pan ()
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Zhang Xiaodan: School of Mathematics and Physics, University of Science and Technology Beijing, Beijing100083, China
Li Ang: School of Mathematics and Physics, University of Science and Technology Beijing, Beijing100083, China
Ran Pan: School of mechanical Engineering, Imperial College London, LondonSW59LS, UK

Journal of Systems Science and Information, 2015, vol. 3, issue 5, 398-410

Abstract: The standard semi-supervised support vector machine (S3VM) is an unconstrained optimization problem of non-convex and non-smooth, so many smooth methods are applied for smoothing S3VM. In this paper, a new smooth semi-supervised support vector machine (SS3VM) model , which is based on the biquadratic spline function, is proposed. And, a hybrid Genetic Algorithm (GA)/ SS3VM approach is presented to optimize the parameters of the model. The numerical experiments are performed to test the efficiency of the model. Experimental results show that generally our optimal SS3VM model outperforms other optimal SS3VM models mentioned in this paper.

Keywords: semi-supervised support vector machine; classification; genetic algorithm; smooth; spline function (search for similar items in EconPapers)
Date: 2015
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Persistent link: https://EconPapers.repec.org/RePEc:bpj:jossai:v:3:y:2015:i:5:p:398-410:n:2

DOI: 10.1515/JSSI-2015-0398

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