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The Progressively Semi-Supervised Classification Model Based on Markov Random Walk

CHEN Xiu-ping;WANG Ming-wen;WAN Jian-yi;ZUO Jia-li;College of Computer Information Engineering,Jiangxi Normal University;School of Elementary Education,Jiangxi Normal University;  
The progressively semi-supervised classification model based on Markov random walk,in the random walk process has been proposed,and calculated the migration probability of samples to be marked,considering only samples of the appropriate category,while ignoring the other classes of samples; and then combined the progressive learning with semi-supervised learning. The model can improve the precision by " correcting" the errors caused in semi-supervised learning process. The results on 20newsgroups dataset in the experiment shows that the proposed method can improve the accuracy of semi-supervised classification.
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