correctly identify said attachment before passing it to sa-learn.
The first part being the toughest. as it's the kind of thing you want to automate or at least
have done for you via a keystroke. So this really becomes an MUA dependent feature.
Obviously in outlook VBA code could be written to construct the message in the appropriate format.
I'm using a mac so I'd be relying on Applescript for this support and in Mail.app I'm not sure Applescript
can get the job done.
On Wednesday, September 24, 2003, at 11:17 PM, Bart Schaefer wrote:
On Wed, 24 Sep 2003, Matt Kettler wrote:
The message must be _exactly_ the same as it originally was, headers and
all. Even very subtle changes can cause the bayes engine to learn things
you might not expect. You want it to learn about ham and spam, not about
forwarded message formats.
[...] it doesn't learn the user.. rather it learns "any forwarded message
is spam". "any message with message headers similar to the ones generated
by this users mail client is spam". You get the picture.
That'd all be true if the users forwarded only spam for learning. If they
forwarded both ham and spam, it'd learn to ignore the characteristics of
local clients and forwarded messages. Right?
It's true that the accuracy of tests involving header tokens will be much
reduced if the format learned is not the same as the format tested. But
if learning really completely fails in these circumstances, it would be
nearly impossible to train, e.g., SAproxy, with messages that have already
passed through Windows mail clients on their way to the disk. And yet
people DO train SAproxy that way, and it seems to work.
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