(bā´ zē-en fil´tër) (n.) A technique for identifying incoming e-mail spam. Unlike other filtering techniques that look for spam-identifying words in subject lines and headers, a Bayesian filter uses the entire context of an e-mail when it looks for words or character strings that will identify the e-mail as spam. Another difference between a Bayesian filter and other content filters is that a Bayesian filter learns to identify new spam the more it analyzes incoming e-mails.
Bayesian filtering is named for English mathematician Thomas Bayes, who developed a theory of probability inference. Bayesian filtering is predicated on the idea that spam can be filtered out based on the probability that certain words will correctly identify a piece of e-mail as spam while other words will correctly identify a piece of e-mail as legitimate and wanted. At its most basic level, a Bayesian filter examines a set of e-mails that are known to be spam and a set of e-mails that are known to be legitimate and compares the content in both e-mails in order to build a database of words that will, according to probability, identify, or predict, future e-mails as spam or not. Bayesian filters examine the words in a body of an e-mail, its header information and metadata, word pairs and phrases and even HTML code that can identify, for example, certain colors that can indicate a spam e-mail.
Bayesian filters are adaptable in that the filter can train itself to identify new patterns of spam and can be adapted by the human user to adjust to the user��s specific parameters for identifying spam. Bayesian filters also are advantageous because they take the whole context of a message into consideration. For example, not every e-mail with the word “cash” in it is spam, so the filter identifies the probability of an e-mail with the word “cash” being spam based on what other content is in the e-mail.
Proponents of Bayesian filters assert that the filters return less than one percent of false positives.
Other forms: Bayesian filtering (v.)
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