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Old 09-16-2012, 09:45 AM
rainbow rainbow is offline
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Default Which kernel to use?

In the course we have applied the gaussian, polynomial and linear kernel on different problems and learned how to tune them wrt. regularization to avoid overfitting.

- For a given problem, it seems like different kernels return different number of support vectors (although with zero training error). Since the generalization ability of the SVM model depends very much on the number of support vectors. Is the actual choice of kernel a "parameter to be tuned" as well?

- Is the choice of kernel application specific, data specific?

- Any rule of thumb?
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