中文摘要：

提出了一种基于高斯混合模型核的半监督支持向量机（SVM）分类算法.通过构造高斯混合模型核SVM分类器提供未标示样本信息,使得SVM算法在学习标示样本信息的同时,能够兼顾整个训练样本集合的聚类假设.实验部分将该算法同传统SVM算法、直推式支持向量机（TSVM）以及随机游走（RW）半监督算法进行分类性能比较,结果证明该算法在拥有较少标示样本训练的情况下分类性能也有所提高且具有较高的鲁棒性.

英文摘要：

The semi-supervised support vector machine（SVM） classification algorithm based on Gauss mixture model kernel is proposed. The unlabeled samples information is provides by constructing the Gauss mixture model kernel SVM classifier. The SVM algorithm is not only study labeled samples information, at the same time, it also can take into account the cluster assumption throughout the training sample set. The comparative experiments are performed with the traditional SVM, transductive SVM and random walk semi-supervised algorithms. The experimental results show that the proposed method not only can improve performance of SVM classification in few training samples, can also increase the overall robust performance.