Bayesian Spam Blocking

Written by Jessica Duquette
Bookmark and Share

Bayesian spam blocking uses mathematical algorithms and probabilities to minimize the number of junk email messages that reach the intended destination. Capitalizing on different enhancements and developer contributions, the filtering software has been able to keep up with unsolicited email. It may sound impossible, but Bayesian filters can actually learn which messages should be treated as spam based on how prior messages are handled.

What this means is that Bayesian spam blocking can detect that the user marks an email as junk email and treat any future message in the same way. The filters in this program also match patterns so that it does not rely on simply one keyword but the text of a message and how those keywords are used. This can save a lot of time for the user because it will result in fewer personal mails being marked as spam and vice versa.

Advancements in Bayesian Spam Blocking

Back in the late 1990s several noted developers spoke about Bayesian filtering and what it could mean about the future of junk email. Since then many developers have analyzed their results and come up with new ways to filter email based on scanning the entire message for keywords.

Have a Little Patience

Although some spam blockers may miss a large number of junk emails when they are first used, the software should be used for a longer period of time. This will allow the system the opportunity to spot spam messages and learn how the user treats them. It may be frustrating at times to still have to look at junk messages in an inbox but in the long run it will pay off.

Bookmark and Share