It isn't possible because a gaussian kernel spans an infinite dimensional
feature space. The coef you are looking for would be functions in that
space.
However, assuming you are working with an SVM (you don't specify this), you
can look at dual_coef_ and if I remember correctly support_ to see which
samples are used as support vectors and with which weights, and then carry
on your reasoning in the dual.
Hope that helps,
Michael
On Wed, Jul 23, 2014 at 2:32 PM, umang patel <17patelum...@gmail.com> wrote:
>
> Hello all ,
>
> I asked the following question on "Issues" and I was advised to mail on
> the following email id I i have furthur queries .
>
> "
> Is it possible to get weight of features in rbf kernel .
>
> It is written under coeff_ that it is possible only with linear kernel .
> Is it mathematically possible to get for rfb kernel , if yes then how ?
> "
>
>
> Could and one please direct me to relevant paper or please explain why it
> is not possible to get coeff_ for rbf kernel .
>
> Thank you.
>
>
>
>
>
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