Adding and removing SVM parameters without having to totally retrain
If you are using SVC from sklearn then the answer is no. There is no way to do it, this implementation is purely batch training based. If you are training linear SVM using SGDClassifier from sklearn then the answer is yes as you can simply start the optimization from the previous solution (when removing feature - simply with removed corresponding weight, and when adding - with added any weight there).
I have a support vector machine trained on ~300,000 examples, and it takes roughly 1.5-2 hours to train this model, and I pickled(serialized) it. Currently, I want to add/remove a couple of the parameters of the model. Is there a way to do this without having to retrain the entire model? I am using sklearn in python.