[osipov]

i think you are looking for document classification algorithms: http://en.wikipedia.org/wiki/Document_classification

the current state of the art algorithms are based on support vector machines, but their learning part could be tricky to implement in a scalable fashion. if you are looking for a quick and dirty approach, TFIDF algorithm (it is a naive "naive Bayes" :) is simple and is adequate for many applications

[osipov]

forgot to mention that you may want to look at the Orange framework which is in Python http://www.ailab.si/orange/

[groovyone]

great! that does look interesting. I'll have a good look through it. You involved in this field yourself? If so, drop me a line as we're looking for a consultant to help us with something

[osipov]

I've done some work on machine learning and specifically document classification in a corporate behemoth. Send me an email to gmail.com and prefix that with osipov followed by an @ sign -- I'll get back to you.

[wehriam]

This sounds interesting to me too, and in an area where I've had some experience. If nothing else perhaps we can trade notes. You'll find contact details in my profile.

[yawl]

I think every language has an open source naive Bayes/Bayes network implementation. And most of the time they are good enough.

KVM (support vector machines) so far is considered the best classification algorithm.

[osipov]

>KVM (support vector machines) so far is considered the best classification algorithm.

In the spirit of accuracy, SVM algorithms aren't _the best_. The best algorithms are ensemble-based, incorporating SVM and alternatives.