Patent ID: 7069257
Filing Date: 2006-06-27
Classification: G06K,G06N

Abstract:
1. A pattern recognition method for reducing classification errors by using a RBF neural network including an input layer, a kernel layer having a plurality of pattern types, each having a plurality of hidden neurons, each corresponding to a kernel function, and an output layer having a plurality of output neurons, each corresponding to a pattern type of the kernel layer, the pattern recognition method comprising the steps of: (A) inputting a feature vector formed by a plurality of training samples to the input layer, wherein the feature vector comprises a plurality of features, each forming a corresponding one of a plurality of input neurons in the input layer; (B) calculating a measured value of the kernel layer relative to the hidden neuron by each of the input neurons based on a kernel function of each hidden neuron; (C) for each output neuron of the output layer, multiplying the measured value of the kernel layer corresponding to the pattern type associated with the output neuron by a weight for obtaining a measured value of the output layer corresponding to the pattern type; (D) finding a maximum one of the measured values of the output layer and a class classification error relative to a correct one of the measured values of the output layer, wherein the class classification error is obtained by calculating an error function E(x)=1−e (E) modifying values of parameters of the kernel function and calculating a weight of the measured values of the output layer based on the error.