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Should One-Class SVM be trained on Positive or Negative examples?
I have a data set containing roughly 1000 examples with 900 negative examples and 100 positive examples. I want to apply one-class SVM and train the model using just one class label. Is there any idea which help me find out whether I should train the model on negative examples or on the positive ones?