Neural Network Architecture Selection: Can Function Complexity Help?

摘要

This work analyzes the problem of selecting an adequate neural network architecture for a given function, comparing existing approaches and introducing a new one based on the use of the complexity of the function under analysis. Numerical simulations using a large set of Boolean functions are carried out and a comparative analysis of the results is done according to the architectures that the different techniques suggest and based on the generalization ability obtained in each case. The results show that a procedure that utilizes the complexity of the function can help to achieve almost optimal results despite the fact that some variability exists for the generalization ability of similar complexity classes of functions.