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TrustRank is an algorithm that conducts link analysis to separate useful webpages from spam and helps search engine rank pages in SERPs (Search Engine Results Pages). It is semi-automated process which means that it needs some human assistance in order to function properly. Search engines have many different algorithms and ranking factors that ...
Naive Bayes classifiers are a popular statistical technique of e-mail filtering. They typically use bag-of-words features to identify email spam, 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 ...
Apache SpamAssassin is a computer program used for e-mail spam filtering. It uses a variety of spam-detection techniques, including DNS and fuzzy checksum techniques, Bayesian filtering, external programs, blacklists and online databases. It is released under the Apache License 2.0 and is a part of the Apache Foundation since 2004.
While most spam emails are being caught by our spam filters, occasionally some can slip through. When this happens, it's important to mark the email as spam. This helps us make AOL Mail even better at recognizing future spam emails. Never interact with spam messages! Any link in a spam message, including the unsubscribe link, could be dangerous.
Various anti-spam techniques are used to prevent email spam (unsolicited bulk email).. No technique is a complete solution to the spam problem, and each has trade-offs between incorrectly rejecting legitimate email (false positives) as opposed to not rejecting all spam email (false negatives) – and the associated costs in time, effort, and cost of wrongfully obstructing good mail.
Distributed Checksum Clearinghouse (also referred to as DCC) is a method of spam email detection. The basic logic in DCC is that most spam mails are sent to many recipients. The same message body appearing many times is therefore bulk email. DCC identifies bulk email by calculating a fuzzy checksum on it and sending that to a DCC server.
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The most notable difference between a conventional Bayesian filter and the filter used by SpamBayes is that there are three classifications rather than two: spam, non-spam (called ham in SpamBayes), and unsure. The user trains a message as being either ham or spam; when filtering a message, the spam filters generate one score for ham and ...