Hey everybody. I just implemented the paper "Random Features for Large-Scale Kernel Machines". It proposes to use Monte Carlo approximations to the kernel mapping to make explicit kernel maps possible. This is awesome since explicit kernel maps make it possible to use SGD classifiers easily.
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. This is currently just for the RBF kernel, but there are other works for intersection kernel, additive chi-squared kernel and skewed chi-squared kernel that I plan to implement. And don't worry, I'm still on the MLP. But I need the feature map approximations any way for my work. 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
