add training data to existing LinearSVC
You cannot add data to SVM and achieve the same result as if you would add it to the original training set. You can either retrain with extended training set starting with the previous solution (should be faster) or train on new data only and completely diverge from the previous solution.
There are only few models that can do what you would like to achieve here - like for example Ridge Regression or Linear Discriminant Analysis (and their Kernelized - Kernel Ridge Regression or Kernel Fischer Discriminant, or "extreme"-counterparts - ELM or EEM), which have a property of being able to add new training data "on the fly".
I am scraping approximately 200,000 websites, looking for certain types of media posted on the websites of small businesses. I have a pickled linearSVC, which I've trained to predict the probability that a link found on a web page contains media of the type that I'm looking for, and it performs rather well (overall accuracy around 95%). However, I would like the scraper to periodically update the classifier with new data as it scrapes.
So my question is, if I have loaded a pickled sklearn LinearSVC, is there a way to add in new training data without re-training the whole model? Or do I have to load all of the previous training data, add the new data, and train an entirely new model?