Hi all,

I hope you don't find this question boring or irrelevant.

I am very interested in SVM. I realize SVM research mainly consists of two parts - the formulation (which is concerned with the choice of regularization and loss function) and the algorithm to solve the formulation (e.g. gradient descent, coordinate descent, trust region newton method).

I have also done a quick survey on available SVM methodologies/packages out in the public: - Liblinear runs very quickly in sacrifice of a little bit of accuracy. - Ramp loss LP SVM uses a relatively innovative loss function known as "ramp loss function" and applies DC programming (difference of convex functions) to solve the formulation, among other techniques. - L1-npsvm (non-parallel proximal support vector machine) uses two hyperplanes instead of one (which is the traditional setting) to achieve amazingly high classification accuracy.

Do you have any comment/opinion on the above? Also, do you know any active research direction on SVM? Please share with me if any. Have a good day!

Sincerely, Mr. F

asked 19 Jan '16, 02:01

bookyeah1679's gravatar image

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edited 19 Jan '16, 02:13

For some reason I am not allowed to comment (I don’t see the point of requiring 50 karma for this). Anyway, you did not mention libsvm in your question. Have you tried that?


answered 19 Jan '16, 06:51

Petter's gravatar image

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Thank you for your reply Petter. I tried libsvm before, and it seems that liblinear is a continuance of libsvm by National Taiwan University. Both can achieve similar classification rate, but liblinear runs much quicker.


answered 19 Jan '16, 14:45

bookyeah1679's gravatar image

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Asked: 19 Jan '16, 02:01

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Last updated: 19 Jan '16, 14:45

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