  Perceptrons with graded input-output relations and a limited output precision
are studied within the Gardner-Derrida canonical ensemble approach. Soft non-
negative error measures are introduced allowing for extended retrieval
properties. In particular, the performance of these systems for a linear and
quadratic error measure, corresponding to the perceptron respectively the
adaline learning algorithm, is compared with the performance for a rigid error
measure, simply counting the number of errors. Replica-symmetry-breaking
effects are evaluated.
