On-line learning through simple perceptron learning with a margin

We analyze a learning method that uses a margin kappa a la Gardner for simple perceptron learning. This method corresponds to the perceptron learning when kappa = 0 and to the Hebbian learning when kappa = infinity. 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.