中文摘要：

针对基于半监督学习的分类器利用未标记样本训练会引入噪声而使得分类性能下降的情形，文中提出一种具有噪声过滤功能的协同训练半监督主动学习算法．该算法以3个模糊深隐马尔可夫模型进行协同半监督学习，在适当的时候主动引入一些人机交互来补充类别标记，避免判决类别不相同时的拒判和初始时判决一致即认为正确的误判情形．同时加入噪声过滤机制，用以过滤由机器自动标记的可能是噪声的样本．将该算法应用于人脸表情识别．实验结果表明，该算法能有效提高未标记样本的利用率并降低半监督学习而引入的噪声，提高表情识别的准确率．

英文摘要：

The classification performance of the classifier based on semi-supervised learning is weakened when the noise samples are introduced. An algorithm called co-training semi-supervised active learning with noise filter is presented to overcome this disadvantage. In this algorithm, three fuzzy buried Markov models are used to perform semi-supervised learning cooperatively. Some human-computer interactions are actively introduced into labelling the unlabeled sample at certain time in order to avoid the rejective judgment when the classifiers do not agree with each other and the inaccurate judgment when the initial weak classifiers all agree. Meanwhile, the noise filter is used to filter the possible noise samples which are labeled automatically by the computer. The proposed algorithm is applied to facial expression recognition. The experimental results show that the algorithm can effectively improve the utilization of unlabeled samples, reduce the introduction of noise samples and raise the accuracy of expression recognition.