Quoting Henry Stern ([EMAIL PROTECTED]):
> I've been thinking a bit about this problem and what we'd have to do to
> the score learning system to accomodate monotonically increasing weights
> for discrete representations of continuous-valued attributes. Rather
> than increasing the complexity of the learner, a much better solution
> (from my perspective) is the following:
>
> * Allow BAYES_00 to have a largeish negative value.
> * Constrain BAYES_05..99 to be >=0.
> * Instead of triggering only BAYES_20 when the output of Bayes.pm is
> 0.2-0.4, trigger BAYES_00, BAYES_05 and BAYES_20.
>
> Comments?
Very reasonable and clever approach to have the BAYES_* rules
represent cumulative additional probabilities. I suspect, however,
that if they've already tried doing the mass check with
a modified weak ordering constraint (00 <= 05 <= 20 <= ... <= 99,
along with BAYES_50 = 0 points), and found that it didn't work
out as expected. I think that's equivalent to your approach.
(I also haven't looked at the code enough to know whether BAYES_50 is
supposed to mean 'there is a 50% chance the message is spam' or '50%
of the evidence from tokens in this message says spam and 50% says ham'
- that is, there's a likelihood ratio of 1. If it's the former, BAYES_50
shouldn't be 0 points, of course, because its score should depend on the
overall base rate of spam vs. ham which varies by recipient. But I sort
of suspect it's the latter.)
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Alan Schwartz <[EMAIL PROTECTED]>
Author/Co-author of: "Managing Mailing Lists", "SpamAssassin",
"Stopping Spam", and "Practical Unix & Internet Security, 3rd Ed"
Published by O'Reilly Media, Inc. (http://www.oreilly.com)
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