2011/12/6 Andreas Mueller <[email protected]>: > On 12/06/2011 04:55 AM, Gael Varoquaux wrote: >> On Mon, Dec 05, 2011 at 10:54:42PM +0100, Olivier Grisel wrote: >>> - libsvm uses SMO (a dual solver) and supports non-linear kernels and >>> has complexity ~ n_samples^3 hence cannot scale to large n_samples >>> (e.g. more than 50k). >>> - liblinear uses some kind of fancy coordinate descent (primal or dual >>> solvers) optimized for regularized linear models, provides more >>> regularization / loss function options such as l1 penalty and can >>> scale to large n_samples (as long as the sparse internal >>> representation of the data fits in memory). >>>> By the way, I suggest someone update the documentation to specify what >>>> the consequences of using the different SVM classes are. Currently >>>> LinearSVC is recommend "for huge datasets", not "for huge sparse >>>> datasets." That is on >>>> this page: >>>> http://scikit-learn.sourceforge.net/dev/modules/generated/sklearn.svm.LinearSVC.html >>> For huge dense data, the only viable option is SGDClassifier on memory >>> mapped arrays (double precision). >> The full content of the above paragraphs should be pasted in the docs >> (with a little bit of rewording). >> > +1
+1 too. I starred this email to remember to do it but please any feel free to do it before I do. -- Olivier http://twitter.com/ogrisel - http://github.com/ogrisel ------------------------------------------------------------------------------ Cloud Services Checklist: Pricing and Packaging Optimization This white paper is intended to serve as a reference, checklist and point of discussion for anyone considering optimizing the pricing and packaging model of a cloud services business. Read Now! http://www.accelacomm.com/jaw/sfnl/114/51491232/ _______________________________________________ Scikit-learn-general mailing list [email protected] https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
