Bayesian Model Selection for Support Vector Machines, Gaussian Processes and Other Kernel Classifiers

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.


Editor(s):
Solla, S.
Leen, T.
Mueller, K.-R.
Published in:
Proceedings of the 13th Annual Conference on Neural Information Processing Systems, 603-609
Presented at:
Neural Information Processing Systems 12, Denver, CO
Year:
2000
Keywords:
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 Record created 2010-12-02, last modified 2018-03-17

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