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P. Sollich (1999)

Probabilistic methods for Support Vector Machines

In: NIPS'99, to appear.

SVMs can be interpreted as MAP-solutions to inference problems with Gaussian Process priors. The evidence for certain hyperparameter values can then be calculated and used to optimize e.g. C, which cannot be set from cross-validation/test error alone; error bars can also be obtained. I show how this helps with the intuitive interpretation of SVM kernels; it also allows Bayesian methods to be used for SVMs (tuning hyper-parameters by maximizing evidence, defining error bars etc).

by admin last modified 2007-01-31 11:08

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