Learning Semi-supervised SVM with Genetic Algorithm

Abstract

Support vector machine (SVM) is an interesting classifier that has an excellent power of generalization. In this paper, we consider SVM in semi-supervised learning. We propose to use an additional criterion with the standard formulation of the transductive SVM for reinforcing the classifier regularization. Also, we use a genetic algorithm for optimizing the objective function, since the transductive SVM yields a non-convex problem. We tested our algorithm on artificial and real data, which gives promising results in comparison with Joachims' algorithm known as SVMlight TSVM.