I understand that. How then does SA treat messages mainly made up of
images?
On Jan 26, 2005, at 10:59 AM, Matt Kettler wrote:
At 11:47 AM 1/26/2005, Jeffrey Lee wrote:
I have been using sa-learn religiously with ALL spam and ham on my
server. However, I keep getting repeat spam with low scores. How can
I increase the sa-learn "points"? So that when I learn a message
instead of increasing some point by .1 or .2 it will increase by .5
or .6?
Well, sa-learning a message doesn't really work by increasing the
"points" of a message, although that's more-or-less the net effect.
In short, you'll want to make sure your inbound messages are hitting
BAYES_90 or higher, and increase the scores of those rules in your
local.cf.
Also, while you're at it, check for spam messages matching
ALL_TRUSTED. If that's happening, check the archives on setting
trusted_networks manually. That rule should *never* match spam but
will if SA gets confused by your MTA config.
If the spam messages are consistently hitting BAYES_99, sa-learning
won't increase the score of that message further, but it does help SA
recognize subtle changes over time in spam. So keep up the training as
it will keep slight deviations from driving the bayes scores down and
causing FN problems that way.
When you sa-learn a message, SA learns that the words in that message
are more likely to be in spam or ham than it previously new. When new
messages come in, SA looks at it's database of words and calculates a
spam probability based on the words in that message. It then matches
that probability to one of the BAYES_* rules and that causes the score
impact.
