公开(公告)号：EP1599781A2

公开(公告)日：2005-11-30

申请号：EP04714607.1

申请日：2004-02-25

Applicant: MICROSOFT CORPORATION

Inventor： ROUNTHWAITE, Robert L. ,  HECKERMAN, David E. ,  MEHR, John D. ,  HOWELL, Nathan D. ,  RUPERSBURG, Micah C. ,  SLAWSON, Dean A. ,  GOODMAN, Joshua T.

IPC: G06F1/00

CPC classification number: H04L51/12 ,  G06Q10/107

Abstract: The subject invention provides for a feedback loop system and method that facilitate classifying items in connection with spam prevention in server and/or client-based architectures. The invention makes uses of a machine-learning approach as applied to spam filters, and in particular, randomly samples incoming email messages so that examples of both legitimate and junk/spam mail are obtained to generate sets of training data. Users which are identified as spam-fighters are asked to vote on whether a selection of their incoming email messages is individually either legitimate mail or junk mail. A database stores the properties for each mail and voting transaction such as user information, message properties and content summary, and polling results for each message to generate training data for machine learning systems. The machine learning systems facilitate creating improved spam filter(s) that are trained to recognize both legitimate mail and spam mail and to distinguish between them.

Abstract translation: 本发明提供了一种反馈回路系统和方法，其有助于在服务器和/或基于客户端的体系结构中与垃圾邮件防止相关联的项目进行分类。 本发明利用机器学习方法应用于垃圾邮件过滤器，特别是随机抽取传入的电子邮件消息，以便获得合法和垃圾/垃圾邮件的示例以产生一组训练数据。 被要求被识别为垃圾邮件机的用户被要求对所接收的电子邮件的选择是否是合法邮件或垃圾邮件进行投票。 数据库存储每个邮件和投票交易的属性，例如用户信息，消息属性和内容摘要，以及每个消息的轮询结果以生成机器学习系统的训练数据。 机器学习系统便于创建改进的垃圾邮件过滤器，该过滤器被训练以识别合法邮件和垃圾邮件并区分它们。