

SVMs : Beginner's resources - apurva

Hey,
So for some one who is starting out with Support Vector Machines, there is just loads of material out there which does not make immediate sense. I found the following resources (may suggest in exploring in the order mentioned) really helpful, maybe they can help you out too :<p>http://www.tristanfletcher.co.uk/SVM%20Explained.pdf<p>http://pyml.sourceforge.net/doc/howto.pdf ( Personally, I found this brilliant and it took some effort to dig this up)<p>Finally:
http://videolectures.net/mlss06tw_lin_svm/<p>Hope this helps..
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Smerity
For a quick introduction to SVMs (and a wide range of machine learning
algorithms) then I highly suggest Andrew Moore's tutorials. They're extremely
concise and well described, taking the format of a university lecture. Only
the PDF slides are available however.
<http://www.autonlab.org/tutorials/svm.html>

If you want a good introduction which actually derives the math and logic
behind SVMs then I'd suggest looking at Stanford's AI/ML video lectures
available for free here -
[http://see.stanford.edu/see/lecturelist.aspx?coll=348ca38a-3...](http://see.stanford.edu/see/lecturelist.aspx?coll=348ca38a-3a6d-4052-937d-cb017338d7b1)
It begins with the first few lectures which covers introductory knowledge and
some other machine learning algorithms but lectures 6-8 cover the theory and
principles behind SVM.

The great thing about this is that relatively little knowledge is assumed on
the student's part and he provides a great deal of notes and handouts on any
areas the students may be fuzzy.

Unless you're going to be merely using a prebuilt machine learning library I
feel that understanding the math and logic behind the algorithms is vital.

~~~
apurva
Good pointers, yes Andrew Ng seems really amazing that way, I had actually
seen his videos too, forgot to post that link.. Thanks guys for these
suggestions.

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vomjom
If you don't care too much about the theory and just want to get up and
running, you should read:

<http://www.csie.ntu.edu.tw/~cjlin/papers/guide/guide.pdf>

And libsvm is probably the most widely used svm library:

<http://www.csie.ntu.edu.tw/~cjlin/libsvm/>

The author of the above paper and library is the same one giving the lecture
in the OP's third link.

~~~
apurva
i did try the libsvm implementation in weka.. but it wasn't available for all
datasets (my guess being it works only for binary classification), ended up
using the SMO algorithm which gave pretty good results too...

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sbt
I would check out some of Thorsten Joachims' projects, try to run them with
some examples, and look at some course notes.

<http://www.cs.cornell.edu/People/tj/>

