@inproceedings{b55535cb52e648249419e4c95c8b66cb,
title = "Curvature-Driven Smoothing in Backpropagation Neural Networks",
abstract = "The standard back-propagation learning algorithm for feed-forward networks aims to minimize 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 generalize 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 minimize such error measures.",
author = "CM Bishop",
year = "1992",
doi = "10.1007/978-1-4471-1833-6_8",
language = "English",
isbn = "978-3-540-19650-1",
series = "Perspectives in Neural Computing",
publisher = "Springer",
pages = "139--148",
editor = "J.G. Taylor and C.L.T. Mannion",
booktitle = "Theory and Applications of Neural Networks",
address = "United Kingdom",
}