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Abstract:
We present a variational Bayesian method for model selection
over families of kernels classifiers like Support Vector machines
or Gaussian processes. The algorithm needs no user interaction and
is able to adapt a large number of kernel parameters to given data
without having to sacrifice training cases for validation. This
opens the possibility to use sophisticated families of kernels in
situations where the small ``standard kernel'' classes are clearly
inappropriate. We relate the method to other work done on Gaussian
processes and clarify the relation between Support Vector machines
and certain Gaussian process models.
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