Parity is not a generalisation problem

Uninformed learning mechanisms will not discover “type- 2” regularities in their inputs, except fortuitously. Clark & Thornton argue that error back-propagation only learns the classical parity problem – which is “always pure type-2” – because of restrictive assumptions implicit in the learning algorithm and network employed. Empirical analysis showing that back-propagation fails to generalise on the parity problem is cited to support their position. The reason for failure, however, is that generalisation is simply not a relevant issue. Nothing can be gleaned about back-propagation in particular, or learning in general, from this failure.