On 11/30/07 12:57 PM, "Kevin Parris" <[EMAIL PROTECTED]> wrote:
> If I have followed the discussion correctly so far, the explanation for > manual-learn not being distinguished from auto-learn is this: no matter what > mode of learning caused a token to appear in the database, if there is ongoing > mail traffic that "hits" on the token then said token will not expire out > anyway. > > In other words, tokens don't expire because of where or how they came to be > listed, they expire because no more incoming mail traffic references them. If > you manually train a message that is the ONLY instance of that particular spam > to slip through your other filter, and your Bayes never sees another message > that matches the tokens it generated, then those tokens are irrelevant > regardless of learn mode. That makes sense, except that if that type of message shows up infrequently, and your token database turns over several times a day because of the high volume of auto-learn... If I've taken the time to send it in for a manual-learn, I'd expect it to be remembered for a while, even if the message only shows up every couple of days. I guess the flip side is that if a message is manually learned, and then you continue to get messages in like that (at least more than the turnover frequency), then the manually-learned information should stay active. Correct? Wes
