2011/11/14 Andreas Müller <[email protected]>: > > If you're still interested in MNIST results: > using gamma=0.03 and C=1 I get 0.9845 with SVC in 12 minutes > (20GB kernel cache, don't know how much was used), > with the same parameters on LinearSVC and 5000 sampled features > I get 0.9783 in ~3 minutes. > Going up to 20000 sampled features gives 0.9822 in ~10 minutes. > That took ~20Gb of ram, though.
Interesting results, thanks for sharing. > I guess using SGD instead of LinearSVC > could speed things up. Indeed SGDClassifier can probably be faster than LinearSVC. You should give it a try. > I also included a small example on the digits dataset in my pull request. > > All in all, I think this is fun to play with and has potentially broad > applicability. +1 -- Olivier http://twitter.com/ogrisel - http://github.com/ogrisel ------------------------------------------------------------------------------ RSA(R) Conference 2012 Save $700 by Nov 18 Register now http://p.sf.net/sfu/rsa-sfdev2dev1 _______________________________________________ Scikit-learn-general mailing list [email protected] https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
