I have 200 training set data with a feature dimension of 1711. I get 50 support vectors; is there a rule of thumb for how many support vectors I should get for N training set data in order for the model to be generalized? Obviously, having 200 support vectors for 200 training set data is bad overfitting. I was wandering if there was a rule of thumb (eg- % of N training data) for number of support vectors.
I came upon this question where it says the number of features may or may not increase the number of support vectors depending on the separability of the data, but that's all I've found so far.