摘要
提出一种包含核函数的Bayesian参数估计方法,提高了Bayesian参数估计的实用性。结合邮件内容和报文格式两个方面分析和提取邮件的重要特征,建立了对应的Bayesian邮件分类网络。将包含核函数的Bayesian参数估计方法应用到邮件分类网络,在对不同邮件测试集的在线学习试验结果证明,这种新的分类模型能够有效地实现垃圾邮件的分类过滤。
A kernel function based Bayesian parameter estimation approach is proposed in this paper which is able to make the algorithm more applicable. Combined with the both sides of email content and format, a Bayesian network for spam classification is well constructed. The testing results by on-line learning for different email testing sets prove that the new model can ensure the classification and filtering efficiently by applying the kernel function based Bayesian parameter estimation approach into the classification network.
出处
《电子科技大学学报》
EI
CAS
CSCD
北大核心
2007年第3期587-589,593,共4页
Journal of University of Electronic Science and Technology of China
