Looking at the kernel function (Gaussian, polynomial. chi-squared, etc) how do we figure out that changing which value will cause overfitting? In my perspective, if we increase (for example) the variance term in Gaussian kernel expression, bias increases and overfit decreases. Is my understanding correct? Can the same thing be done for chi-squared and polynomial functions?
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