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

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@Corporate_Cowboy wrote:

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!

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