On 11/11/2011 04:04 PM, Gael Varoquaux wrote: > On Fri, Nov 11, 2011 at 04:00:54PM +0100, Andreas Müller wrote: >> I just implemented the paper "Random Features for Large-Scale Kernel >> Machines". > The Rahimi and Recht one :). It's been on my desktop, waiting for > implementation for something like a year. > Maybe I should have given the authors *cough* well good you knew the work any way. >> I was wondering whether this would be interesting for sklearn to >> include. The technique is pretty easy and it takes about 10-20 lines to >> implement with a fit / transform interface. > 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. I'll try some more datasets. A computer vision group at my university (Cristian Sminchisescu's group) is using a similar technique, called skewed chi2 kernel, for their segmentation algorithms. They won Pascal VOC this year with it, so I guess it should be some good.
The method only makes sense for dense datasets, I am afraid, so I'll see what I can find. Cheers, Andy ------------------------------------------------------------------------------ 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
