2011/11/11 Gael Varoquaux <[email protected]>: > On Fri, Nov 11, 2011 at 04:11:46PM +0100, Andreas Müller wrote: >> > If you find that it does work/is useful on real problem, yes! >> I just started working on it. Atm I can get 3% error on MNIST using >> sklearn's SGD. > > Does sound good.
Doesn't a grid-searched gaussian SVM yield 2% or 1.5% test error on MNIST? If the CPU efficiency is much better than SVM then it's still good to have even if less accurate than kernel SVM. > I find that one of the values of the scikit, and in particular its > mailing list, is that empirical knowlegde that comes to coding and trying > many methods. I am definitely exciting about the random features methods, > as well as the Chi2 one of your colleagues. +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
