Estimating the size of neural networks from the number of available training data

Estimating a priori the size of neural networks for achieving high classification accuracy is a hard problem. Existing studies provide theoretical upper bounds on the size of neural networks that are unrealistic to implement. This work provides a computational study for estimating the size of neural networks using as an estimation parameter the size of available training data. We will also show that the size of a neural network is problem dependent and that one only needs the number of available training data to determine the size of the required network for achieving high classification rate. We use for our experiments a threshold neural network that combines the perceptron algorithm with simulated annealing and we tested our results on datasets from the UCI Machine Learning Repository. Based on our experimental results, we propose a formula to estimate the number of perceptrons that have to be trained in order to achieve a high classification accuracy.