A model is proposed in which the synaptic efficacies of a feedforward neural network are adapted with a cost function that vanishes if the boolean function that is represented has the same symmetry properties as the target one. The function chosen according to this procedure is thus taken as an archetype of the whole symmetry class. Several examples are presented showing how this type of partial learning can produce a behaviour of the net that is reminiscent of that of dyslexic persons.
Asymptotic Prediction Error Variance for Feedforward Neural Networks
