A distance mapping pattern classification method

The invention relates to a distance mapping pattern classification method. The established distance mapping classifier comprises four parts, namely an input layer, a hidden layer 1, a hidden layer 2 and an output layer. The input layer comprises d neuron nodes, and each node represents a feature vector of a training or test sample. The feature vector set of the extracted samples is used as the input layer of the distance mapping classifier. The hidden layer 1 contains N neuron nodes, N representing the total number of training samples; and calculates the Euclidean distance between the input eigenvector and the eigenvectors of N training samples as the hidden layer 1. The hidden layer 2 consists of L neuron nodes. The hidden layer 1 and the hidden layer 2 can be connected by linear activation function. The two parameter matrices of the linear activation function are the connection weights and offsets of the hidden layer 1 and 2 respectively. When the classifier model is used to classifythe test samples, the ou