Hi James,
if by chance your combination of kernels yields one of the built-in kernels
for SVM ('linear', 'poly', 'rbf', 'sigmoid', see
https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/svm/classes.py#L221),
then yes ... :)
Otherwise you will have to pass a new kernel function / precomputed kernel
to the estimator you are using, provided it accepts them (which is the case
e.g. for `sklearn.svm.SVC`)
Hope that helps
Michael
On Mon, Nov 17, 2014 at 10:26 AM, James McMurray <jamesmc...@gmail.com>
wrote:
> Hi,
>
> Is it possible to combine kernels in the SVM in Scikit-learn, i.e. if I
> wanted a sum of different kernels, or different kernels on different
> dimensions, etc.?
>
> Or would I need to define a new kernel?
>
> Best,
> James McMurray
>
>
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