关键词: 半监督学习, 协同训练, Tri-training, 支持向量机, 最小二乘支持向量机

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 co-training will be better.Theoretical analysis and experiments show that the proposed algorithm has excellent accuracy and speed of classification.

Key words: semi-supervised learning, co-training, Tri-training, Support Vector Machine（SVM）, least square support vector machine