next up previous
Next: Guidelines and Rules Up: Learning in Feed-Forward Previous: Conjugate Gradients

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.



System PRIVILEGED Account
Fri Feb 24 11:28:59 MET 1995