I
saw an article a while back about some DJs who were using perl as a mixing tool
by writing perl code that edited itself while it ran in a loop. I thought this
was kind of cool.
I
studied AI at university, and remember a good bit of discussion regarding feedback systems.
So,
to combine the two, I was thinking of how to use SA in a similar structure, and
propose a dynamic weighting system for SA rules. Consider the scores that a base
installation of SA gives to its rules, but when shown messages to learn from, it
modifies the score weighting of the rules rather than the bayes
system.
I'll
not throw out a discussion regarding learning rates and so, but I can imagine
the logic being loosely based on how much influence the rule had on the total
score, the distance of the final result from the spam/ham boundary, and the learning rate chosen by the administrator.
Any
feedback?
R
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