  We investigate the generalization ability of a simple perceptron trained in
the off-line and on-line supervised modes. Examples are extracted from the
teacher who is a non-monotonic perceptron. For this system, difficulties of
training can be controlled continuously by changing a parameter of the teacher.
We train the student by several learning strategies in order to obtain the
theoretical lower bounds of generalization errors under various conditions.
Asymptotic behavior of the learning curve has been derived, which enables us to
determine the most suitable learning algorithm for a given value of the
parameter controlling difficulties of training.
