Particle Swarm Optimization for Semi-supervised Support Vector Machine

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 (SPSVM). The approximation performance of the smooth piecewise function is better than the Gaussian approximation function. According to the non-convex character of SPSVM, 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.