<SNIP> for my money, the best time spent is following the *pattern* based filters and working on ways to share that information amongst others of like interest. a good start would be a site dedicated to the sharing of procmail recipes, beysian formulas, etc. <SNIP>
Yes, this sounds like a good starting point. No need to debate the value of open-relays; there is a need, but not in the public circuit. but to bring this back to James, we are looking at implementing mailets to process SPAM. So far, the process is to consult black-hole lists and to feed mail through a Bayesian filters and what not. One problem I see with this is that black-hole lists are arbitrary and pattern matches are too inclusive. Spam filtering needs to be based on user preference as each instance will be different; to quote " Bayesian spam filters are content-based filters that - are specifically trained to recognize the individual email user's spam and good mail, making them highly effective and difficult to adapt to for spammers. - can continually and without much effort or manual analysis adapt to the spammers' latest tricks. - take the individual user's good mail into account and have a very low rate of false positives. Unfortunately, if this causes blind trust in Bayesian anti-spam filters, it renders the occasional mistake even more serious. " (http://email.about.com/library/weekly/aa100702a.htm) The effective SPAM blocker system will be bayesian based user specific systems. James currently touts a Bayesian mailet, but employs only an overall data source and is not concerned with individual preference; to be an effective SPAM blocker, a relationship needs to be established between a specific user and her Bayesian lists. and of course the necessary functionality to maintain the lists ... --------------------------------------------------------------------- To unsubscribe, e-mail: [EMAIL PROTECTED] For additional commands, e-mail: [EMAIL PROTECTED]
