  We analyze a learning method that uses a margin $\kappa$ {\it a la} Gardner
for simple perceptron learning. This method corresponds to the perceptron
learning when $\kappa=0$, and to the Hebbian learning when $\kappa \to \infty$.
Nevertheless, we found that the generalization ability of the method was
superior to that of the perceptron and the Hebbian methods at an early stage of
learning. We analyzed the asymptotic property of the learning curve of this
method through computer simulation and found that it was the same as for
perceptron learning. We also investigated an adaptive margin control method.
