A neural network model with bounded-weights for pattern classification
A new neural network model is proposed based on the concepts of multi-layer perceptrons, radial basis functions, and support vector machines (SVM). This neural network model is trained using the least squared error as the optimization criterion, with the magnitudes of the weights on the links being limited to a certain range. Like the SVM model, the weight specification problem is formulated as a convex quadratic programming problem. However, unlike the SVM model, it does not require that kernel functions satisfy Mercer's condition, and it can be readily extended to multi-class classification. Some experimental results are reported. For the past decade, there has been increasing interest in solving nonlinear pattern classification problems. Among the various approaches, Multi-layer perceptrons, radial basis function networks and support vector machines have received most attention due to their tremendous success in real-world applications. Compared with the other two, The support vector machines approach is relatively new and often performs better in many applications. However, it also has some limitations, for example, kernel functions are required to satisfy Mercer's condition and it is not easily applicable for multi-class classification. In this paper, we propose a new neural network model which overcomes these limitations.
neural networks pattern classification multi-layer perceptrons support vector machines radial basis function networks