Experiences with Bayesian Learning in a Real World Application
Peter Sykacek, Georg Dorffner, Peter Rappelsberger, Josef Zeitlhofer · 1997
This paper reports about an application of Bayes' inferred neural network classifiers to the field of automatic sleep staging. Up to our current knowledge this is one of the first real world applications of Bayesian inference. We therefore want to share our experience of this learning paradigm with a wider audience. 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 was not better than a single maximum a-posteriori (MAP) solution found with D.J. MacKay's evidence approximation (see [6]). In a second experiment we studied the properties of both solutions in rejecting classification of movement artefacts. 1 1 Category: Application...