基于Markov随机游走的渐进式半监督分类模型
The Progressively Semi-Supervised Classification Model Based on Markov Random Walk

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.
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