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中文摘要：
提出了一种基于Markov随机游走的渐进式半监督分类模型：在随机游走过程中，计算待标注数据到各类的迁移概率时，只考虑相应类别样本的影响，而忽略其他类别样本对随机过程的影响；并在学习过程中借鉴渐进学习思想，通过不断地“纠正”半监督学习过程中的“错误”，从而提高模型的预测精度．在20newsgroups数据集上的实验结果表明：所提出的方法能够提高半监督分类的精度．
英文摘要：
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 sam- pies 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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