Hi all, How am I supposed to work with multiple set of normal data for one-class SVM? If I have two normal scenario data set, A and B for learning phase, should I create predictor model separately (M(A) + M(B)) or can I combine A and B to create just a single predictor model (M(A+B))?
I have try both approach using one-class SVM in scikit-learn, and my results shows that FPR for combined normal data set is significantly higher (more than 30% in average) than separate prediction (suggesting that separate prediction is better than combined prediction). I just want to confirmed this findings, is that how it supposed to be? Are there any way to improved combined prediction model for one-class SVM? Thank you in advance. Best regards, Ady ------------------------------------------------------------------------------ _______________________________________________ Scikit-learn-general mailing list Scikit-learn-general@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
