Hi,
I really liked this primer from Nature Biotechnology, which I think gets
the point across intuitively in only 3 pages:
William S Noble
What is a support vector machine?
Nature Biotechnology 24, 1565 - 1567 (2006)
http://www.broadinstitute.org/annotation/winter_course_2006/index_files/Noble%202006%20SVM%20tutorial%20Nat%20Biotech.pdf
http://www.nature.com/nbt/journal/v24/n12/abs/nbt1206-1565.html
Brian
doi:10.1038/nbt1206-1565
Message: 2
Date: Mon, 16 May 2011 18:05:25 +0200
From: Thorsten Kranz <[email protected]>
Subject: [pymvpa] Introduction to Machine Learning and SVMs
To: Development and support of PyMVPA
<[email protected]>
Message-ID: <[email protected]>
Content-Type: text/plain; charset=UTF-8
Hi all,
I have a question, maybe you have a quick reply (to a non-trivial
question though...).
Here in my lab, some colleagues without too much knowledge in
mathematics would like to learn (and understand) some basics of
machine learning and SVMs in particular, so we'll have a little
methods-seminar soon. I will try to explain it to them, but it would
be nice if I could send them some kind of tutorial-paper or
book-chapter they could read before that.
Do you have any proposal for that? I know of the Hastie et al. book
online, but maybe "less mathematics" would fit better to (some) of my
colleagues.
Thanks in advance, greetings,
Thorsten
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