| Interesting trick. Unfortunately that does not work anymore on | non-thresholded gray level pixels that are more common in non-| | toy computer vision datasets.
Sparse coding can do the similar trick to those datasets. | LinearSVC will convert to Liblinear's custom sparse format | internally, so it's no surprise that it's faster with many | zeros. I understand that the data is smaller is the sparse format in the sense of: (1 0 0 0 0 0 0 0 1) -> (1:1 10:1) Does this fact enough to make the classifier faster? I used to think that the training time has something to do with the difficulty of the classification problem. By simply inverting the 0 and 1, do we actually affect the difficulty? Or maybe I understand it wrongly? -- Caleb ------------------------------------------------------------------------------ Flow-based real-time traffic analytics software. Cisco certified tool. Monitor traffic, SLAs, QoS, Medianet, WAAS etc. with NetFlow Analyzer Customize your own dashboards, set traffic alerts and generate reports. Network behavioral analysis & security monitoring. All-in-one tool. http://pubads.g.doubleclick.net/gampad/clk?id=126839071&iu=/4140/ostg.clktrk _______________________________________________ Scikit-learn-general mailing list Scikit-learn-general@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
