John, by 'spam corpus' are you referring to the 'spam' side of the Bayesian filter? If we manually delay/review these known-bad accounts are we creating a window of opportunity for those same messages to pass through to current users?
I've been assuming we would need to create an intentional delay (e.g. 60 second) on all Amavis processing, combined with an exception for the known-bad addresses wherein they would immediately get added to the Bayesian filter. Does the Bayesian update in real-time as you feed it new spam or do you need to request a periodic rebuild? ___________ Also I still don't understand why everyone is so reticent to immediately black-list messages based on these 100% known-bad addressess. For instance, is it possible for a bulk spam message to trigger false positives? There is zero concern about valid company clients/contacts mistakenly emailing these ex-employees, e.g. these were entry-level staff who did not interact with clients and just used their office email for personal use (and got themselves onto lots of spam lists in the process). Also, we can pick ex-staff who have been gone more than 5 years if necessary. I mean, we literally are 100% positive their incoming email is spam... -- View this message in context: http://spamassassin.1065346.n5.nabble.com/train-filter-based-on-spam-to-ex-employees-tp114546p114558.html Sent from the SpamAssassin - Users mailing list archive at Nabble.com.
