Journal of Physics A. Mathematical and General vol:35 issue:9 pages:2093-2109
We study supervised learning and generalization in coupled perceptrons trained on-line using two learning scenarios. In the first scenario the teacher and the student are independent networks and both are represented by an Ashkin-Teller perceptron. In the second scenario the student and the teacher are simple perceptrons but are coupled by an Ashkin-Teller-type four-neuron interaction term. Expressions for the generalization error and the learning curves are derived for various learning algorithms. The analytical results find excellent confirmation in numerical simulations.