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Abstract:
I describe a framework for interpreting Support Vector
Machines (SVMs) as maximum a posteriori (MAP) solutions to
inference problems with Gaussian Process priors. This can provide
intuitive guidelines for choosing a "good" SVM kernel. It can also
assign (by evidence maximization) optimal values to parameters such
as the noise level C which cannot be determined unambiguously from
properties of the MAP solution alone (such as cross-validation
error). I illustrate this using a simple approximate expression for
the SVM evidence. Once C has been determined, error bars on SVM
predictions can also be obtained.
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