# Estimating hyperparameter in basis functions (Gaussian and sigmoid) for linear regression

I am working on a linear regression problem with Gaussian and sigmoid basis functions. My data set is very large, say a total of 15K inputs with each input having 46 features. I have divided my data into three sets as usual Training, CV and Test. By plotting the ERMS vs Degree of Polynomial for all three sets I chose the degree. But I am clueless about how to choose the parameters ($S$ and $\mu$) in basis functions. I am using the following: $$F_{\text{Gaussian Basis}}(x[j])= \exp\left(\frac{(x-\mu(j))^2}{S^2}\right).$$ For now I am choosing them randomly. Any suggestion or good link where I can read about this?

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