# Turn a distance measure into a kernel function

I have read here that an easy way to turn a distance function $d$ into a similarity function $s$ is to compute: $s = e^{-\gamma * d}$. I believe that this is also what is done with the RBF kernel. There the distance function is the squared eucledian distance $||x - y||^2$ and the corresponding kernel function is: $e^{-\gamma * ||x - y||^2}$. However I'm unable to find a name for this kind of method and I can't find any scientific papers where this method is introduced or applied. Can you point me to some papers with more information ?

(Notice that the distance function I want to turn into a similarity function (kernel function) is not a distance metric. I'm also aware that the resulting kernel function might not be a valid psd kernel.)