Particle Swarm Optimization for Semi-supervised Support Vector Machine
QING WU1,+, SAN-YANG LIU+ AND LE-YOU ZHANG+ 1School of Automation Xi'an Institute of Posts and Telecommunications Xi'an, Shaanxi, 710121 P.R. China +Department of Mathematical Sciences Xidian University Xi'an, Shaanxi, 710071 P.R. China
Semi-supervised Support vector machine has become an increasingly popular tool for machine learning due to its wide applicability. Unlike SVM, their formulation leads to a non-smooth non-convex optimization problem. In 2005, Chapelle and Zien used a Gaussian approximation as a smooth function and presented TSVM. In this paper, we propose a smooth piecewise function and research smooth piecewise semi-supervised support vector machine (SPS3VM). The approximation performance of the smooth piecewise function is better than the Gaussian approximation function. According to the non-convex character of SPS3VM, a converging linear particle swarm optimization is first used to train semi-supervised support vector machine. Experimental results illustrate that our proposed algorithm improves TSVM in terms of classification accuracy.