期刊文献+

基于图像信息度量与关键词的邮件智能分类系统

Intelligent Email Classification System Based on PIM and Keywords
下载PDF
导出
摘要 如何利用邮件的正文与附件信息有效地实现其分类,是现在邮件处理领域一个重要的课题。该文从商业应用角度提出了一种基于图像信息度量与关键词的邮件智能过滤与分发方法,通过基于朴素贝叶斯分类器的邮件关键词信息处理,及附件图像信息的基于归一化PIM文本图像检测理论的分析,能够综合运用邮件正文、地址等文本信息与附件图像信息作为分类的评价参数,有效地实现了邮件的智能分类。 It is an important technology to classify emails by using text and appendix information in the field of email processing. This paper presents an intelligent email classification system based on PIM and keywords. Keywords are used as the input features of native Bayes classifier to filter text of email. PIM is used as the statistical feature to classify images in appendixes. Experiments show that this system is efficient in intelligent emails classification.
出处 《计算机工程》 CAS CSCD 北大核心 2007年第15期199-201,共3页 Computer Engineering
关键词 多用途网际邮件扩充协议 BASE64 图像信息度量 朴素贝叶斯 multipurpose Internet mail extensions(MIME) Base64 picture information measurement(PIM) Native Bayes
  • 相关文献

参考文献5

  • 1Mock K.An Experimental Framework for Email Categoritation and Management[C]//Proceedings of the 24th Annual International ACM SIGIR Conference on Research an Development in Information Retrieval.2001:392-393.
  • 2詹川,卢显良,周旭,侯孟书,袁连海.基于贝叶斯公式的垃圾邮件过滤方法[J].计算机科学,2005,32(2):73-75. 被引量:11
  • 3Meyer T A,Whateley B.SpamBayes:Effective Open-source,Bayes Based,Email Classification System[C]//Proceeding of the 1st Conference on Email and Anti-spam.2004.
  • 4童莉,平西建.基于信息度量的图像特征与文本图像分类[J].计算机工程,2004,30(17):143-145. 被引量:7
  • 5Cheng Juan,Ping Xijian.Text Image Retrieval Based on Generalized Normalized Picture Information Measure[C]//Proc.of the IEEE Natural Language Processing and Knowledge Engineering.2005.

二级参考文献10

  • 1[2]Diligenti M B, Frasconi P, Gori M. Hidden Tree Markov Models for Document Image Classification. PAMI(25), 2003,(4):5189-523
  • 2[3]Carmagnac F, Heroux P, Trupin E. A Document Image Classification Strategy Based on Distance Computation and Feature Selection. 3rd International Workshop on Pattern Recognition in Intormation Systcms, PRIS, 2003:179-184
  • 3中国互联网络信息中心.第十三次《中国互联网络发展状况统计报告》[R].,2004,1..
  • 4上海艾瑞市场咨询公司.中国反垃圾邮件市场研究报告[R].,2003,11..
  • 5.[EB/OL].http://www. ai. mit. edu/~jrennie/ifile/.,.
  • 6Sahami M, Dumais S,et al. A Bayesian Approach to Filtering Junk E-Mail. Learing for Text Categorization -Papers from the AAAI Workshop,Madison Wisconsin, 1998.
  • 7Chen Duhong, Tongjie, et al. Spam Email Filter Using Naive Bayesian, Decision Tree, Neural Network and AdaBoost. http://www. cs. iastate. edu/~tongjie/spamfilter/paper. pdf.
  • 8Androutsopoulos I,Paliouras G,et al. Learning to filter spam email : a comparison of a naive Bayesian and a memory-based approach. In:Proc. of the workshop "Machine Learning and Textual Information Access", 4th European Conf. on PKDD-2000, Lyon,France, Sep. 2000.
  • 9Langley P,Wayne I,Thompson K. An Analysis of Bayesian Classifiers. In: Proc. of the 10thNational Conf. on Artificial Intelligence,San Jose,California, 1992.
  • 10Domingos P ,Pazzani M. On the Optimality of the Simple Bayesian Classifier under Zero-One Loss. Machine Learning, 1997,29:103 ~130?A?A.

共引文献16

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部