SVMs : Beginner's resources
5 comments
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... 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.
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... 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.
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.
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.
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.
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...
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/
http://www.cs.cornell.edu/People/tj/
http://www.tristanfletcher.co.uk/SVM%20Explained.pdf
http://pyml.sourceforge.net/doc/howto.pdf ( Personally, I found this brilliant and it took some effort to dig this up)
Finally: http://videolectures.net/mlss06tw_lin_svm/
Hope this helps..