Curvature-Driven Smoothing in Backpropagation Neural Networks
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TL;DR
A modified error measure is proposed which can reduce the tendency to over-fit and whose properties can be controlled by a single scalar parameter.
Abstract
The standard backpropagation learning algorithm for feedforward networks aims to minimise the mean square error defined over a set of training data. This form of error measure can lead to the problem of over-fitting in which the network stores individual data points from the training set, but fails to generalise satisfactorily for new data points. In this paper we propose a modified error measure which can reduce the tendency to over-fit and whose properties can be controlled by a single scalar parameter. The new error measure depends both on the function generated by the network and on its derivatives. A new learning algorithm is derived which can be used to minimise such error measures.