Hi All,
I would like to compute the norm of weight vector w for the Support
Vector Regression algorithm. I am correct in thinking that it can be
computed in the following way?
clf = SVR(C=1.0, epsilon=0.2, kernel="rbf", gamma=g)
clf.fit(X, y)
v = np.squeeze(clf.dual_coef_)
SV = clf.support_vectors_
K = sklearn.metrics.pairwise.rbf_kernel(SV, SV, g)
norm = np.sqrt(v.T.dot(K).dot(v))
Is there some way to get the norm without recomputing the kernel matrix
entries of the support vectors?
In addition, I would like to use the same norm in model selection for
training on the whole set of examples. For example, if I use 5 fold
cross validation for model selection, then parameters are selected using
4/5 of the training set, but the selected parameters are used in
conjunction with the whole training set. I would like to use model
selection to pick the norm of w with the lowest error and then use the
same norm when training on the entire training set. How might this be
achieved? One way I can think of is to try a number of C values on the
whole training set and then pick the one with norm closest to that found
during model selection.
Thanks in advance for any help,
Charanpal
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