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Abstract:
We present a new approach to bounding the true error rate of a
continuous valued classifier based upon PAC-Bayes bounds. The
method first constructs a distribution over classifiers by
determining how sensitive each parameter in the model is to
noise. The true error rate of the stochastic classifier found
with the sensitivity analysis can then be tightly bounded using a
PAC-Bayes bound. In this paper we demonstrate the method on
artificial neural networks with results of a 2-3 order of
magnitude improvement vs. the best deterministic neural net
bounds.
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