So recently I wrote this code: https://github.com/jaberg/asgd/blob/early_stopping/asgd/linsvm.py
My intent with this class was to provide a sklearn-like interface to train linear SVMs, but which would have automatic selection logic to handle various problem dimensions, which call for different algorithms: * if you have more features than examples, you should use a gram-matrix algorithm, * if you don't then you should use an sgd-type algorithm * if you have more than two classes, you should use a larank-type algorithm (i think?), but ... * if you have to use a gram-matrix algorithm for efficiency then I wonder if maybe you can't do larank so you should use a one-vs-all approach (or one vs. one?). Anyway this code uses SVC in some cases, and uses @npinto's asgd code in other cases, and uses some of my code in others... but I have a feeling that I'm reinventing a wheel here, is there something in sklearn that already does this type of thing? - James ------------------------------------------------------------------------------ This SF email is sponsosred by: Try Windows Azure free for 90 days Click Here http://p.sf.net/sfu/sfd2d-msazure _______________________________________________ Scikit-learn-general mailing list [email protected] https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
