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Particular words have particular probabilities of occurring in spam email and in legitimate email. This assumption gives rise to the widely-used Naive Bayes classifier. that the probability of a word occurring in a document is assumed (incorrectly) to be independent of the occurrence or absence of any other word. Therefore a simplifying independence assumption is frequently made i.e. Pr ( s p a m | w o r d s ) = Pr ( w o r d s | s p a m ) Pr ( s p a m ) Pr ( w o r d s ) terms is not usually feasible due to the vast number of possible word combinations that may occur in an email. Bayes' theorem, in the context of spam, says that the probability that an email is spam, given that it has certain words in it, is equal to the probability of finding those certain words in spam email, times the probability that any email is spam, divided by the probability of finding those words in any email: 4 General applications of Bayesian classificationīayesian classifiers take advantage of Bayes Theorem.The regular incidence of spam with selections of lengthy normal text passages from books, etc, is an attempt to corrupt this process by modifying the occurrences of 'non-spam' words in spam messages.
ASSP VS SPAMASSASSIN SOFTWARE
Server-side email filters, such as SpamAssassin and ASSP, make use of Bayesian spam classification techniques, and the functionality is sometimes embedded within mail server software itself. Many modern mail programs implement Bayesian spam filtering. Since then it has become a popular mechanism to distinguish illegitimate spam email from legitimate email. (1998) and gained attention in 2002 when it was described in a paper by Paul Graham. Contributors are invited to replace and add material to make this an original article.īayesian message classification is the process of using Bayesian statistical methods to classify documents into categories.īayesian message classification was proposed to improve spam filtering from Sahami et al. The content on this page originated on Wikipedia and is yet to be significantly improved.