If one trains a model using a SVM from kernel data, the resultant trained model contains support vectors. Now consider the case of training a new model using the old data already present plus a small amount of new data as well. SO:
Should the new data just be combined with the support vectors from the previously formed model to form the new training set? (If yes, then how to combine the support vectors with new graph data? I am working on
Should the new data and the complete old data be combined together and form the new training set and not just the support vectors of the old one?
Which approach is better for retraining, and/or is more doable and efficient in terms of accuracy and memory?