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Old 09-16-2012, 12:18 PM
Andrs Andrs is offline
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Join Date: Jul 2012
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Default Re: Which kernel to use?

Quote:
Originally Posted by JohnH View Post
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.
If you do not know much about the data and you are using svm, RBF is a good kernel to start with, you may select some other kernels (linear..)and/or parameters. If you have a reasonable number of kernel alternatives, you may use cross validation to select the kernel that produces the smallest E_cv. CV to select a kernel (among diff options) can be used with a limited amount of data.
The lowest E_cv, should be a good measurement for generalization for the selected kernel.
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