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From fork-admin@xent.com  Thu Aug 22 10:47:10 2002

this just struck me as odd, but interesting in a way i guess.
A LifeGem is a certified, high quality diamond created from the carbon of
your loved one as a memorial to their unique and wonderful life.

Robert Harley:
>>BTW, I wasn't aware that the 1st Amendment mandated that crap must be
>>FoRKed.
Chuck Murcko <chuck@topsail.org>:
>It doesn't, BTW. It says the right to free speech shall not be abridged.
>That means *you* can't say anything may not be FoRKed or printed or
>whatever.
Actually, it means just the opposite. The first
amendment guarantees Harley's right to say just
that. For the outlets where he has editorial
control, it even guarantees his right to CENSOR
content published through those outlets. The
first amendment doesn't limit Harley's speech,
and it is neutral with regard to the selection
policies of FoRK and other private venues.
_________________________________________________________________
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http://photos.msn.com/support/worldwide.aspx

[Skip Montanaro]
> Because I get mail through several different email addresses, I
> frequently get duplicates (or triplicates or more-plicates) of
> various spam messages.  In saving spam for later analysis I haven't
> always been careful to avoid saving such duplicates.
>
> I wrote a script some time ago to try an minimize the duplicates I see
> by calculating a loose checksum, but I still have some duplicates.
> Should I delete the duplicates before training or not?
People just can't stop thinking <wink>.  The classifier should work best
when trained on a wholly random spattering of real life.  If real life
contains duplicates, then that's what the classifier should see.
> Would people be interested in the script?  I'd be happy to extricate
> it from my local modules and check it into CVS.
Sure!  I think it's relevant, but maybe for another purpose.  Paul Svensson
is thinking harder about real people <wink> than the rest of us, and he may
be able to get use out of approaches that identify closely related spam.
For example, some amount of spam is going to end up in the ham training data
in real life use, and any sort of similarity score to a piece of known spam
may be an aid in finding and purging it.