Adaptive training methods for optimal margin classification

The concept of optimal hyperplane has been recently investigated in the context of statistical learning theory. The important property of an optimal hyperplane is that it provides maximum margins to each class to be separated. Obviously, such a decision boundary is expected to yield good generalization. In neural network learning techniques, the majority of them do not make use of the optimal hyperplane concept. As a result, in many cases extensive tuning is required to reach good generalization. In this study we consider adaptive training schemes for optimal margin classification with neural networks. We describe some novel schemes and compare them with the conventional schemes. Simple experiments are presented to demonstrate the performance of each scheme