Computer Science ›› 2015, Vol. 42 ›› Issue (10): 235-238.

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Robust Smooth Support Vector Machine

HU Jin-kou and XING Hong-jie   

  • Online:2018-11-14 Published:2018-11-14

Abstract: Smooth support vector machine (SSVM) is regarded as an improved model of the traditional support vector machine.SSVM utilizes the smooth technique to reformulate the quadratic programming problem of the traditional support vector machine as an unstrained optimization one.Moreover,the Newton-Armijo algorithm is used to solve the unstrained optimization problem.In the paper,on the basis of SSVM,robust smooth support vector machine (RSSVM) was proposed by utilizing M-estimator to substitute the L2-norm based regularization term of SSVM.Furthermore,the half-quadratic minimization method is used to solve the corresponding optimization problem of RSSVM.Experimental results demonstrate that the proposed method can efficiently enhance the anti-noise capability of SSVM.

Key words: Smooth support vector machine,Half-quadratic minimization,Kernel function

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