On Mittwoch, 10. Mai 2006 17:27 Bowie Bailey wrote:
> So you are saying that I should not feed Bayes with the unsolicited
> marketing garbage that I get because it looks like something that
> could have been requested?

If it's a newsletter from a seemingly legit company I don't feed it to 
bayes. I try to unsubscribe from them. If they still send me, I write 
some rule to filter them. If some customer then rants, I tell them that 
said company doesn't work nicely - and he should make a filter to get 
e-mail from that company out of the SPAM folder again.

> > Remember: 10 good SPAM and HAM are better than 200 where 5% are
> > wrong.
> Wrong for who?  If it looks like marketing, 99% of the time, I don't
> want it.  And for most of the accounts that I deal with, this goes up
> to 100%.  Not true for my customers, tho.

Yes, some manual filters can catch those. If it's stupid SPAM, then 
bayes.

> My philosophy with Bayes has always been to skip the ham/spam
> definitions and go with a wanted/unwanted model.  This way Bayes
> learns to filter out the emails you don't want even if some of them
> may technically be ham.  (Obviously, I would not be able to do this
> on a site-wide installation)

But as you said your bayes is not quite accurate, so it seems not to 
work really. Wouldn't it be better to have a highly accurate bayes, and 
setup some filters for you personally? If a BAYES_99 would be always 
SPAM for you, you could give it 4.5 or 5 points, and probably filter 
more SPAM than now?

> But then again, I think less than half of my users are even taking
> advantage of the spam markup.  Since I don't do any blocking or
> sorting on the server, it is up to them to use MUA rules to sort or
> delete the spam once my server has marked it.

I do the same, just wrote a nice document for Outlook 2003 describing 
how to filter SPAM.

mfg zmi
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