Not written as a tutorial per se, but I've found the discussion in this
paper useful for helping people understand how the machine learning
approach works in a general/conceptual sense:
Breiman, L., 2001a. Statistical modeling: the two cultures. Stat. Sci.
16, 199–215.
Jo
On 5/16/2011 11:09 AM, Yaroslav Halchenko wrote:
IIRC the best video describing SVMs (with math though) is
http://videolectures.net/mlss06tw_lin_svm/ from Support Vector
Machines author:Chih-Jen Lin, National Taiwan University
who is an author of libsvm
as for "less math" -- need to think about it... and it would depend
on what aspects of SVM you want them to accent in comparison to other
methods
On Mon, 16 May 2011, Thorsten Kranz wrote:
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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