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  • Casual Articles - Stop Spam - A Simple Trick To Get The Most Out Of Your Bayesian Filter

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    a 'spam score' to each word of each message. The average 'spam score' is then used to determin
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    If you sat a person infront of a computer, and asked them to remove all the spam messages in someone's inbox, you would expect them to achieve close to 100% accuracy. This implies that there is something innate in a spam email that is recognisable as 'spammy'.

    This is the idea that Bayesian Filters work on. They process emails before they are delivered to your inbox, assigning a 'spam score' to each word of each message. The average 'spam score' is then used to determine

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    eone's inbox, you would expect them to achieve close to 100% accuracy. This implies that there is something innate in a spam email that is recognisable as 'spammy'.

    This is the idea that Bayesian Filters work on. They process emails before they are delivered to your inbox, assigning a 'spam score' to each word of each message. The average 'spam score' is then used to determin

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    s something innate in a spam email that is recognisable as 'spammy'.

    This is the idea that Bayesian Filters work on. They process emails before they are delivered to your inbox, assigning a 'spam score' to each word of each message. The average 'spam score' is then used to determin

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    Bayesian Filters work on. They process emails before they are delivered to your inbox, assigning a 'spam score' to each word of each message. The average 'spam score' is then used to determin
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    a 'spam score' to each word of each message. The average 'spam score' is then used to determine whether the email is to be spammed or not.

    Bayesian Filters are used by most email providers in some manner. Generally ISP's will use some form of Bayesian filter to skim the most blatant spam messages off the top, and then leave the filtering of the marginal messages to the email user themselves. Webmails use very efficiently Bayesian filters which draw on both individual a

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