Network complexity and generalization
Sang-Bong Park, Cheol Hoon Park · Proceedings of International Conference on Neural Networks (ICNN'97) · 2002
This paper explains the relationship between complexity of the neural network with sigmoidal hidden neurons and its generalization capability in function approximation. Network complexity is decided in terms of the number of degrees of freedom and their dynamic range. Computer simulation shows that dynamic range as well as degrees of freedom affects training and generalization capability.