[pymvpa] Introduction to Machine Learning and SVMs

[Brian Murphy]

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 <thorstenkranz at googlemail.com>
> Subject: [pymvpa] Introduction to Machine Learning and SVMs
> To: Development and support of PyMVPA
>         <pkg-exppsy-pymvpa at lists.alioth.debian.org>
> Message-ID: <BANLkTimto1YNZPwGcqchv2vHtcgUyNLFJQ at mail.gmail.com>
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>
> 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
>
>
>