Semi-supervised Collaborative Training Algorithm Based on Graph
Tao Guo, Guiyang Li, Lan Xia · Jisuanji gongcheng · 2012
In classifier training process,the introduction of unlabeled data can cause noise data,and it reduces classification accuracy.This paper proposes Confidence Estimation for Semi-supervised Learning based on graph(CESL) algorithm.The algorithm makes use of structure information of sample data to calculate classification probability of unlabeled data explicitly.Combined with multi-classifiers,the algorithm estimates the confidence of unlabeled data implicitly and improves the selection criteria.With dual-confidence estimation,the unlabeled data is selected to update classifiers.Experiments on UCI datasets prove the efficiency of this algorithm.