The kernel equations in Support Vector Machines are given by :
In the RBF kernel, parameter ‘gamma’ is present.
I found it online that as we decrease the value of gamma, the variance of a model increases and it starts overfitting the data. Can anyone please share a resource explaining why this happens in terms of the kernel function mathematically?
Any help would be appreciated!