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http://bugs.kde.org/show_bug.cgi?id=55835
------- Additional Comments From os xn--ball-epa net 2008-03-06 10:04 -------
This reminds me of an idea I had a few months ago. The whole process of email
filtering could be done using supervised learning (basically, the same way a
bayes spam filter such as bogofilter works).
Meaning, the user would initially just assign a few mails to folders (or at
some later point in time create a new folder and move some messages to it), and
the system would learn similarities between the messages in the folders based
on their headers. Then, when a new message arrives, the system would do a
classification and move the new message to the best matching folder. Of course,
the user should be given a way to correct classification errors once in a while.
This scheme would be extremely easy to understand even for novice users, since
all they have to do is set "examples" for the system to learn from.
I've had a very good experience with the bogofilter (only once in a while it
yields a false negative, and I've had no false positives at all for about two
years), so I can't think of a reason why a similar scheme for regular email
filtering shouldn't work. What do you think?
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