Classifying unlabeled data with SVMs

Abstract

SVMs have been used to classify the labeled data. While in many applications, to label data is not an easy job. In this paper, a new quadratic program model is presented so that SVMs can be used to classify the unlabeled data. Based on this model, a new semi-supervised SVM is also presented. The experiments show that the new semi-supervised SVM can be used to improve the correct rate of classifiers by introducing the unlabeled data. © 2006 Springer.