摘要
综合分析了垃圾邮件过滤的技术路线与方法,并在分析基于关键字的方法和统计学的方法的基础上,提出了将两者相结合,运用模式识别中的贝叶斯、最近邻和感知机等分类方法,实现对垃圾邮件的过滤的技术路线。以互信息最大化准则筛选出的特征集为基础,对不同分类技术的对比分析揭示了贝叶斯、最近邻和感知机在垃圾邮件过滤应用上的优劣。同时,文中对基于互信息最大化准则的垃圾邮件过滤应用提出了有益的思路。
Paths to solving and methods of filtering unsolicited bulk e-mails,also known as spam,have been analyzed.And the method based on keyword and the statistical learning have been analyzed.Then a new method which is a combination of the two methods have been proposed.The method to filter spam using the naive Bayesian decision theory,the nearest-neighbor classification,and the linear classification based the perceptron criterion function which is used in pattern classification has been introduced.The feature set used in the three theories have been gotten by mutual information.By comparied the three decision theories,the advantages and disadvantages of them has been presented.At same time,a good idea to filtering spam using mutual information has been pointed out in the paper.
出处
《计算机工程与应用》
CSCD
北大核心
2007年第25期128-132,共5页
Computer Engineering and Applications
基金
国家自然科学基金(the National Natural Science Foundation of China under Grant No.60602038)
广东省自然科学基金( the NaturalScience Foundation of Guangdong Province of China under Grant No.06300862) 。
关键词
垃圾邮件
分类器
贝叶斯
最近邻
感知机
spam mail
classification
Bayesian decision
nearest-neighbor decision
perceptron criterion function
