On 07/09/2012 01:32 PM, Philipp Singer wrote: > Am 09.07.2012 13:59, schrieb Vlad Niculae: >> Another (hackish) idea to try would be to keep the labels of the extra >> data bit give it a sample_weight low enough not to override your good >> training data. > That's actually a great and simple idea. Would I do that similar to that > example: > http://scikit-learn.org/stable/auto_examples/svm/plot_weighted_samples.html > > So like using a 10 times higher weight for the corresponding samples for > example as a starting point? > > I see that the fit method of LinearSVC doesn't have a sample_weight > parameter. So I guess I would have switch to another method. SVC > unfortunaetly has a very long runtime compared to LinearSVC, but maybe a > SGDClassifier would work. > You can use SVC with kernel="linear". That shouldn't be much slower than LinearSVC.
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