On-line learning through simple perceptron with a margin

Abstract

We analyze a learning method that uses a margin $κ$ {\it a la} Gardner for simple perceptron learning. This method corresponds to the perceptron learning when $κ=0$, and to the Hebbian learning when $κ\to \infty$. 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.