# Gaussian prior from feature to input space

if I have Gaussian prior ($\exp\left(\dfrac{-\sum_i w_i}{2\gamma^2}\right)$) on my weights in a linear classifier, how can I transform this so I can apply it for my kernel parameters $\alpha$? I have seen various papers mentioning this but I don't see how it's actually done. :/

Thanks!

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