Quantitative analysis of generalization ability of neural network with prior-knowledge

Abstract

The theoretical analysis of neural network generalization is presented in this paper with techniques based on the PAC learning and the VC dimension. Generalization indexes are proposed to measure the approximation correctness of trained neural networks for given target functions. Neural networks with prior knowledge are discussed on the basis of the generalization measurement, and are proved to possess better generalization ability compared with ordinary networks. They usually require considerably fewer training examples while keeping better generalization performance