Semi-supervised active learning algorithm for SVMs based on QBC and tri-training

For the problem that large-scale labeled samples are not easy to acquire in the course of Support Vector Machines (SVMs) training, a Semi-Supervised Active Learning Algorithm for SVMs (QTB-ASVM) is proposed in the paper, which efficiently combines the semi–supervised learning based on Tri-Training and active learning based on Query By Committee (QBC) with SVMs. With this method, QBC active learning is used to select the samples which are the most valuable to current SVM classifier, and Tri-Training is used to exploit useful information that remains in the unlabeled samples. The experimental results show that the proposed approach can considerably reduce the labeled samples and costs compared to the SVMs which is either not applied with semi-supervised learning or active learning or applied with only one of them, and at the same time it can ensure that the accurate classification performance is kept as the passive SVM, while improving generalization performance and also expediting the SVM training.