A Parallel Bi-Perceptron Approach and Its Application to Data Classification

Since the kernel function in support vector machine is arbitrary, it carries no physical meaning in practical applications. This work presents a bi-perceptron network that works in real physical space. All network parameters can be obtained in a constructive way without training. It is a divide-and-conquer way with perfect performance. We show how to operate this network to classify records.