Using RBF kernel in SVM with high Gamma value. What does this signify?

@sachinkalsi wrote:

I was going through this article. And here goes the question

10. Suppose you are using RBF kernel in SVM with high Gamma value. What does this signify?

And the answer in the article is: The model would consider only the points close to the hyperplane for modeling.

Actually gamma or sigma in RBF is nice approximation to K in KNN. so if the sigma value is high, then the model would consider even far away points.

You can try plotting using plot(exp(-x^2/(2*sigma^2))) in google.

Any explanations ?

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