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Abstract:
This paper reports about an application of Bayes' inferred
neural network classifiers in the field of automatic sleep staging.
The reason for using Bayesian learning for this task is two-fold.
First, Bayesian inference is known to embody regularization
automatically. Second, a side effect of Bayesian learning leads to
larger variance of network outputs in regions without training
data. This results in well known moderation effects, which can be
used to detect outiers. In a 5 fold cross-validation experiment the
full Bayesian solution found with R. Neal's hybrid Monte Carlo
algorithm was not better than a single maximum a-posteriori (MAP)
solution found with D.J. MacKay's evidence approximation. In a
second experiment we studied the properties of both solutions in
rejecting classification of movement artifacts.
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