Rebuilding the Bayesian Learning Process in SecurityGateway

SecurityGateway and MDaemon both feature Bayesian learning, which allows administrators (or users, when authorized) to feed samples of spam and non-spam email messages to designated public folders. By default, when 200 samples of spam and 200 samples of non-spam have been placed in these folders, the Bayesian learning process will process these folders and feed their contents to a database of what are known as tokens – snippets of spam-like and ham-like (non-spam) content, basically. We all know that we humans are not infallible – people make mistakes, so it’s possible for messages to be fed to the wrong folders. When this happens, users may begin to receive more false-negatives (spam that was not caught by the spam filter) or you may accumulate a number of false positives (legitimate email messages that were flagged as spam by the spam filter). When this happens, it may be necessary to rebuild the Bayesian database. You may recall that I posted  a blog entry awhile back on how to rebuild the Bayesian database for MDaemon. You can read that post here. For SecurityGateway, the concepts are the same, but the navigation and file locations are different. The following tutorial video explains how to rebuild the Bayesian database in SecurityGateway.

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