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Interpretation of the Results

Even though the target values t in a classifying problem are binary, the output units in an MLP will not take values that are exactly 1 or 0. However, one can interpret these outputs in a very useful way; they correspond to Bayesian a posteriori probabilities [24] provided that:

  1. The training is accurate.
  2. The outputs are of 1-of-M-type (the task is coded such that only one output unit is ``on'' at a time).
  3. A mean square, cross entropy or Kullback error function is used.
  4. Training data points are selected with the correct a priori probabilities.
This very important result enables the network outputs to be further processed in a controlled way. In the case of function mapping the output error can be estimated with standard methods based on distances to cluster centers in the training data.

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Fri Feb 24 11:28:59 MET 1995