基于Tri_training的半监督SVM.pdfVIP

计算机工程与应用 ， （ ） 103 Computer Engineering and Applications 2009 45 22 基于Tri-training 的半监督SVM 李昆仑，张 伟，代运娜 ， ， LI Kun-lun ZHANG Wei DAI Yun-na 河北大学电子信息工程学院，河北 保定 071002 ， ， ， ， College of Electronic and Information Engineering Hebei University Baoding Hebei 071002 China ： E-mail wzhanghbu@ ， ， LI Kun-lun ZHANG Wei DAI Yun-na.Semi-supervised SVM based on Tri-training.Computer Engineering and Applica－ ， ， （）： tions 2009 45 22 103-106. ： ， Abstract One of the main difficulties in machine learning is how to solve large-scale problem effectively and the labeled data are limited and fairly expensive to obtain.In this paper a new semi-supervised SVM is proposed.It applies Tri-training to improve SVM.The semi-supervised SVM uses a few labeled data to train few initial SVM classifiers and makes use of the large number unlabeled data to modify the classifier iteratively.Experiments on UCI dataset show that Tri-training can improve the classification ， accuracy of SVM and can increase the difference of classifier the accuracy of final classifier will be higher.Although Tri-training ’ ， doesn t put any constraints on the supervised learning algorithm the proposed method uses the SVMs with three different kernel ， functions as the supervised learning algorithm.The different kernel can increase the difference of the three SVMs so the performance of