累积反馈学习的简单贝叶斯垃圾邮件过滤

NAVE BAYESIAN SPAM FILTERING BASED ON ACCUMULATIVE FEEDBACK LEARNING

给出了一种基于累积反馈学习的简单贝叶斯邮件过滤方法。在此基础上,通过领域规则的引入,对基于累积反馈学习的简单贝叶斯过滤方法进行了改进。实验结果表明累积反馈学习对不断保持和提高分类器的分类效果是必要的。

In this paper it proposes a Naive Bayesian spam filtering algorithm based on accumulative feedback model. Applying some domain rules in our accumulative feedback Naive Bayesian spam filter, the filter＇s performance is improved. Experimental results indicate that the accumulative feedback method is necessary on maintaining and enhancing the classifier＇s effect.