On Fri, Sep 28, 2012 at 2:32 PM, Andreas Mueller
<[email protected]>wrote:
> 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.
>
My humble point of view is that "exact" SVM/SVR are a bit overkill in
general if:
- you're training linear model (SGD is obviously better)
- you're training a big non linear problem problem (n_samples > 1000). In
that case I'd look
at random forest or gbm ...
For small non linear problems having an exact SVM/SVR solver
(not approximated) is very useful IMHO.
Paolo
------------------------------------------------------------------------------
Got visibility?
Most devs has no idea what their production app looks like.
Find out how fast your code is with AppDynamics Lite.
http://ad.doubleclick.net/clk;262219671;13503038;y?
http://info.appdynamics.com/FreeJavaPerformanceDownload.html
_______________________________________________
Scikit-learn-general mailing list
[email protected]
https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
