Dear All.
Please put on sunglasses before opening the openopt webpage.
Also: I think the way forward with SVMs is using low rank approximations
of the kernel matrix.
For "small" datasets, SMO or the version in LASVM seem to work very well
imho.
Cheers,
Andy
Am 28.09.2012 10:53, schrieb Paolo Losi:
Hi all,
I'm following the thread about libsvm...
I just wanted to share some impressive result I got by solving svm
with OpenOpt [1].
My main use case was to try different loss functions for regression
(libsvm only
provides epsilon insensitive).
In a couple of hours I succeeded in implementing an SVR solver that is
competitive
with libsvm (x4 slower in all cases I tested. I used ralg optimization
algo).
If anyone is interested I could share a gist ...
Ciao
Paolo
[1] http://openopt.org <http://openopt.org/Welcome>
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