2012/2/15 Ian Goodfellow <[email protected]>: > Further update: I talked to Adam Coates and his code doesn't implement > a standard SVM. Instead it's an "L2 SVM" which squares all the slack > variables. So this probably explains the difference in performance I > observed prior to building this test case.
Good to know. AFAIK this is the same loss (squared hinge) as used by default in liblinear. You could try to compare the outcome with LinearSVC (albeit you will get the memory copy of you dense input) or SGDClassifier (the penalty and loss parameters will allow you to adjust the objective function to your will). -- Olivier http://twitter.com/ogrisel - http://github.com/ogrisel ------------------------------------------------------------------------------ Virtualization & Cloud Management Using Capacity Planning Cloud computing makes use of virtualization - but cloud computing also focuses on allowing computing to be delivered as a service. http://www.accelacomm.com/jaw/sfnl/114/51521223/ _______________________________________________ Scikit-learn-general mailing list [email protected] https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
