Approximation and estimation bounds for artificial neural networks

For a common class of artificial neural networks, the mean integrated squared error between the estimated network and a target function f is shown to be bounded by O \left ( {{C^2_f}\over n}\right ) + O\left ({nd\over N}{\rm log}\ N\right), where n ...

Function approximation, which finds the underlying relationship from a given finite input-output data is the fundamental problem in a vast majority of real world applications, such as prediction, pattern recognition, data mining and classification. ...