I'm in a situation where I need to limit the number of support vectors (SVs) in my support vector machine binary classifier. A simple way to do this would be to manually remove the least important SVs. Does anyone know of a way to do this?
I've tried manually modifying the following fields of the SVM to remove SVs, but without success:
clf.dual_coef_ clf.intercept_ clf.n_support_ clf.support_ clf.support_vectors_ clf.shape_fit_
clf is my RBF SVM.
Note that increasing regularization (by decreasing C) does not necessarily reduce the number of SVs (https://dgroppe.com/2018/01/21/increased-regularization-does-not-necessarily-decrease-the-number-of-support-vectors/)