R help - Nov 2006 - variable selection with support vector machines (SVM)

Hello

I am using support vector machine (from package kernlab) for a classification
task (with RBF-Kernel). My data has dozens of variables and I need to identify
which variables contribute most to the classification performance.

What I did so far is comparing the classification performance (measured for
example with the proportion of misclassified cases) of different sets of
variables with cross-validation. Unfortunately this is very slow and doing, for
example, a backward variable selection procedure will take half a day with my
data.

This raises 3 interrelated questions:

Does someone know an alternative way to perform variable selection in the
context of SVM-classification ?

Does someone know of an R-function that automatizes variable selection for SVM ?

Is there a way to quantify the contribution of every single variable to the
classification performance ?   (I guess there is no short answer to this but I
would also be very happy on references of good articles or books on this topic)


Thanks a lot in advance,

Serge Zaugg


  
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