贝叶斯网络在过滤垃圾邮件算法中的应用研究

为在用户数据流中删除垃圾邮件，研究了具有自我学习能力的自适应邮件过滤系统．在对正常和垃圾2类邮件误分类成本分析的基础上，利用概率性的学习方法创建满足过滤任务需要的过滤器，且讨论使用邮件域名特征变量进行特定邮件过滤并设计了过滤器，最后对实际邮件组进行操作，验证了算法的可靠性．

Bayesian application study on arithmetic for filtering junk e-mail

To delete spams from User data stream,an adaptive self-learning spam filtering system has been studied and presented.On the basis of cost analysis mistakenly classified of normal and spam e-mail,a filter to meet the requirements of filtering tasks with learning methods of probabilistic is created,and the use of mail domain name characteristic variables for a particular e-mail filtering is studied.Finally,experiments verify the reliability of the algorithm.