> -Original Message-
> From: [EMAIL PROTECTED]
> that's exactly what Bayes does, effectively ;) Given the amount of
> training you put in, it makes reliable decisions based solely on
> that data, taking into account the *amount* of training that has
> been performed etc.
Since my knowl
Basically you
1. extract the time-of-day from the Received header.
2. normalize to nearest 2-hour mark.
3. add that in the Bayes message tokenizer, to the list of tokens.
The Bayesian learner then allows you to "train" on your local mail
collection, which you've classified into ham and s
Larry Gilson writes:
> However, if one tries to automate this then thresholds need to be attained
> automatically. So even if Bayes learns the time, a filtration engine needs
> to be able to analyze the spam/ham over time. A time minimum time interval
> needs to be met before thresholds can be a
Hey Justin,
Fuzzy Fox suggested a similar route. The Bayes token is a great
possibility. The tokens in this case would be time rather than words.
One way to accomplish this task is to just give local.cf assignments that
would score during a specific time interval. This would allow the
administ
Larry Gilson writes:
>I believe there is a big problem awaiting those who generalize one graph.
>This graph will most definitely change from organization to organization.
>The pattern, however may be similar. This poses a huge problem when trying
>to develop a general rule for the SA community.
Hi Regis,
> -Original Message-
> From: C. Regis Wilson
> HOWEVER! Your graph is excellent and points out a flaw with
> your (and my) suggestion. The graph clearly shows when peak Ham
> flows. It does not tell you when Spam flows! We're looking at the
> problem backward. :) You see,
>Graphing spam and ham amounts, with the help of spamstats
>(http://www.gryzor.com/tools), I notice the legitimate
>emails mostly arrive from about 9am, until maybe 7pm, on
>working days.
>
I have written about this and others too. The correct procedure is to
submit some proposed code to the dev