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Old 09-16-2012, 10:27 AM
JohnH JohnH is offline
Join Date: Jul 2012
Posts: 43
Default Re: Which kernel to use?

Although only briefly mentioned in the lectures, machine selection of appropriate kernels is one of the approaches that may be taken. The caveat is that considering additional kernels increases the complexity of \mathcal H and thus requires larger data sets to mitigate the risk of overfitting. It is possible that multiple kernels could be applied with the output of each being aggregated to produce the final model.

I suspect that selection of a kernel, without snooping in the data, is more art than science, but may be guided by one's understanding (read intuition) of the expected characteristics of the data.
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