  We study the generalization ability of a simple perceptron which learns
unlearnable rules. The rules are presented by a teacher perceptron with a
non-monotonic transfer function. The student is trained in the on-line mode.
The asymptotic behaviour of the generalization error is estimated under various
conditions. Several learning strategies are proposed and improved to obtain the
theoretical lower bound of the generalization error.
