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Naive Bayes classifiers are a popular statistical technique of e-mail filtering. They typically use bag of words features to identify spam e-mail, an approach commonly used in text classification. Naive Bayes classifiers work by correlating the use of tokens (typically words, or sometimes other things), with spam and non-spam e-mails and then using Bayes' theorem to calculate a probability that an email is or is not spam. Naive Bayes spam filtering is a baseline technique for dealing with spam that can tailor itself to the email needs of individual users and give low false positive spam detection rates that are generally acceptable to users. It is one of the oldest ways of doing spam filtering, with roots in the 1990s. ==History== The first known mail-filtering program to use a naive Bayes classifier was Jason Rennie's ifile program, released in 1996. The program was used to sort mail into folders.〔(【引用サイトリンク】author=Jason Rennie )〕 The first scholarly publication on Bayesian spam filtering was by Sahami et al. in 1998. That work was soon thereafter deployed in commercial spam filters. However, in 2002 Paul Graham greatly decreased the false positive rate, so that it could be used on its own as a single spam filter.〔Paul Graham (2003), (Better Bayesian filtering )〕〔Brian Livingston (2002), (Paul Graham provides stunning answer to spam e-mails )〕 Variants of the basic technique have been implemented in a number of research works and commercial software products.〔(【引用サイトリンク】title=Junk Mail Controls )〕 Many modern mail clients implement Bayesian spam filtering. Users can also install separate email filtering programs. Server-side email filters, such as CRM114, DSPAM, SpamAssassin, SpamBayes, Bogofilter and ASSP, make use of Bayesian spam filtering techniques, and the functionality is sometimes embedded within mail server software itself. 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「Naive Bayes spam filtering」の詳細全文を読む スポンサード リンク
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