Just throwing out a general question. What do people think of applying feature selection methods when using SVMs to build predictive models? I understand that SVM have built in regularization with how they're trained, but I've heard for certain cases (e.g. features >> examples) feature selection is still useful. I'm mainly talking about non-linear SVMs (e.g. kernelized).
Is there a difference in effectiveness of feature selection when say # features >> # examples vs. # features ~ # examples vs. # features << examples?
If you don't think feature selection is useful, please explain why. If you think feature seleciton is useful (and have practical experience in this regard), please let us know what methods you've used and how they worked.
Also open to entirely different approaches to non-linear predictive modeling other than SVMs.