Hi Gordon > The rationale for spamassassin's behaviour is, I think, the fear that > in unsupervised mode it will go off track. Perhaps there should be a user > flag "supervised/unsupervised" that determines whether or not the same > criteria are used for filtering and learning. In "supervised" mode > the learner should use the same criteria as the filter. Otherwise the > learner cannot be properly trained.
Seconded. I've seen the same effect (with SA 2.55 though); after initial (manual/supervised) training the filter worked great. running with autolearn for a while significantly reduced the efficiency. Another problem I have with autolearn: when viewing the message, you can't tell if (and as what) it's already beed fed into the bayes database. I implemented essentialy the same thing you did using a different method: I use SA from MIMEDefang, and just added a local ham/spam mailbox to the list of recipients depending on the result of SA. Combined with manual feedback for the few false classifications that slip through, this seems to work very well. Bye, Martin ------------------------------------------------------- This SF.Net email is sponsored by: INetU Attention Web Developers & Consultants: Become An INetU Hosting Partner. Refer Dedicated Servers. We Manage Them. You Get 10% Monthly Commission! INetU Dedicated Managed Hosting http://www.inetu.net/partner/index.php _______________________________________________ Spamassassin-talk mailing list [EMAIL PROTECTED] https://lists.sourceforge.net/lists/listinfo/spamassassin-talk
