Risk bound of priority ordered neural network with multi-weighted neurons

Neural networks are widely used in different fields. However, fixed architecture is difficult to use in practice for its pre-determined neurons and architectures. Constructive architecture is proposed in this paper for neural network based on idea of human´s cognition where each neuron has its own priority number and evolution of learning process with time. Using prediction theory, risk bound of the architecture with multi-weighted neurons are analyzed. Experimental results show that the propose method outperforms the SVM method using limited samples. The proposed method is constructive and this work can provide a very useful method for neural network learning model.