What is the reason for effect of gamma parameter in non-linear classification using Support Vector Machines?

svm
mathematics
kernel

#1

Howdy,

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!


#2

Hi Corporate_Cowboy,

You can refer to this link
http://scikit-learn.org/stable/auto_examples/svm/plot_rbf_parameters.html

Hope this will help you to understand.


#3