Is there a way to manually remove support vectors from a scikit-learn model?
I was having the same problem. I managed to do it with Scikit's OneClassSVM; maybe it can also help in your case. 
I adjusted some of the parameters you mentioned plus a few more (just in case) including:


_dual_coef_
dual_coef_
_intercept_
intercept_
support_
support_vectors_


Ex: For OneClassSVM, if you obtained 10 support vectors (after fitting dataset X) and wanted to prune the last one (index -1) the code would be as follows: 

model = OneClassSVM()
model.fit(X)

model._intercept_ = model._intercept_ + temp_model.dual_coef_[0][-1]
model.intercept_ = model.intercept_ + temp_model.dual_coef_[0][-1]
model._dual_coef_ = np.delete(model._dual_coef_, -1, axis=1)
model.dual_coef_ = np.delete(model.dual_coef_, -1, axis=1)
model.support_ = np.delete(model.support_, -1, axis=0)
model.support_vectors_ = np.delete(model.support_vectors_, -1, axis=0)