基于社会计算和机器学习的垃圾邮件快速过滤

在对当前垃圾邮件过滤方法进行研究和分析的基础上，本文将社交网络的概念用于垃圾邮件识别，并提出了一种将社会计 算和机器学习相结合的垃圾邮件过滤方法，以减少垃圾邮件的误判率.为了提高邮件过滤的实时性，我们利用Hadoop平台 所提供的MapReduce模型进行分布式并行处理.对 比实验结果表明，我们所采用的识别方法的识别准确率和识别效率都有较大的提高，尤其是降低了正常邮件的误判率.

The quick spam filtering method based on social computing and machine learning

Based on the current research and analysis of spam filtering methods, this paper uses the concept of social network for spam recognition and puts forward a kind of spam filtering method combing social computing and machine learning to reduce the misjudgment rate of spam emails. In order to improve the real-time performance of the filters, we use the MapReduce model which is provided by the open source cloud computing platform Hadoop to process in distributed and parallel. Contrast experiment results show that, our recognition method has a great improvement in accuracy and efficiency of recognition, especially on reducing the misjudgment rate for normal mail.