中文摘要：

这份报纸建议学习算法基于的新奇基于图的 transductive 歧管规则化。首先，歧管规则化为监督半的分类任务被介绍给概率的判别式模型。然后期望最大化(他们) 的一个变化算法被导出解决优化问题，它导致一个反复的算法。尽管我们的方法在概率的框架被开发，没有需要关于数据分发的特定的形式做假设。而且，关键更新公式关上了形式。这个方法在二标准数据集， 20 新闻组和 Reuters-21578 上为文章分类被评估。实验证明我们的途径超过学习方法的最先进的基于图的 transductive。

英文摘要：

This paper proposes a novel graph-based transductive learning algorithm based on manifold regularization. First, the manifold regularization was introduced to probabilistic discriminant model for semi-supervised classification task. And then a variation of the expectation maximization （EM） algorithm was derived to solve the optimization problem, which leads to an iterative algorithm. Although our method is developed in probabilistic framework, there is no need to make assumption about the specific form of data distribution. Besides, the crucial updating formula has closed form. This method was evaluated for text categorization on two standard datasets, 20 news group and Reuters-21578. Experiments show that our approach outperforms the state-of-the-art graph-based transductive learning methods.