Smooth support vector machine based on piecewise function  

作　　者：WU Qing FAN Jiu-lun 

机构地区：[1]School of Automation, Xi'an University of Posts and Telecommunications [2]School of telecommunication and information engineering, Xi'an University of Posts and Telecommunications

出　　处：《The Journal of China Universities of Posts and Telecommunications》2013年第5期122-128,共7页中国邮电高校学报（英文版）

基　　金：supported by the National Natural Science Foundation of China (61100165, 61100231, 61105064, 51205309);the Natural Science Foundation of Shaanxi Province (2012JQ8044, 2011JM8003, 2010JQ8004);the Foundation of Education Department of Shanxi Province (2013JK1096)

摘　　要：Support vector machines （SVMs） have shown remarkable success in many applications. However, the non-smooth feature of objective function is a limitation in practical application of SVMs. To overcome this disadvantage, a twice continuously differentiable piecewise-smooth function is constructed to smooth the objective function of unconstrained support vector machine （SVM）, and it issues a piecewise-smooth support vector machine （PWESSVM）. Comparing to the other smooth approximation functions, the smooth precision has an obvious improvement. The theoretical analysis shows PWESSVM is globally convergent. Numerical results and comparisons demonstrate the classification performance of our algorithm is better than other competitive baselines.Support vector machines （SVMs） have shown remarkable success in many applications. However, the non-smooth feature of objective function is a limitation in practical application of SVMs. To overcome this disadvantage, a twice continuously differentiable piecewise-smooth function is constructed to smooth the objective function of unconstrained support vector machine （SVM）, and it issues a piecewise-smooth support vector machine （PWESSVM）. Comparing to the other smooth approximation functions, the smooth precision has an obvious improvement. The theoretical analysis shows PWESSVM is globally convergent. Numerical results and comparisons demonstrate the classification performance of our algorithm is better than other competitive baselines.

关 键 词：SVM smooth technique piecewise function bound of convergence 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]