> From: Bryan Hoover
> Sent: Friday, January 09, 2004 10:52 PM
[...]
>
>
> Gary Funck wrote:
> >
> > > From: Robert Menschel
> > Here's an idea that I've been considering for a while: have SA
> change its
> > scoring strategy to use a Neural Net, instead of using the
> strictly additive
> > scoring. SA would still use its custom rules to detect spam
> markers, but it
>
> Just a couple comments that may or may not be on the same page...
>
> It is an idea that sort of captures the imagination.  Though, isn't that
> very similar to what's already being done with the GA, as well as
> Bayes?

Not that similar. The GA as I understand it, is using an AI technique
to optimize a linear equation - which is given by: Sum(score[i]) for
each i-th rule. This means that a rule is constrained to have the same
weight in all contexts. Let's say that a given rule is not a good
determinant
of spam when seen with rules A and B, but is a good determinant of spam when
seen with rules C and D. In the current framework, the rule would get the
same weighting in both contexts, but a trained neural network is capable of
differentiating those situations and giving a higher weightin in the (C,D)
context. Also, NN's use a non-linear function that a function that has a
sharp cutoff in the middle, thus the NN will tend to disambiguate close
calls
more sharply than a linear function would.

> The GA sets scores according to what turns out to be most
> accurate - least FPs, FNs.  Bayes anaylizes selected email tokens to
> this end.  What would you feed the nueral net for learning?
>

See above on the GA. The Bayes would still be a component in the scoring
as it is now. Bayes represents the adapative aspect of SA, and would
continue to do so.

One difficulty of a NN approach is that it wouldn't work well with new
custom rules without retraining, and for that reason it may be unsuitable
for many situations.






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