@inproceedings{c345932cda054fc99b85ee1c5d16fec6,
title = "Prediction Error of a Fault Tolerant Neural Network",
abstract = "For more than a decade, prediction error has been one powerful tool to measure the performance of a neural network. In this paper, we extend the technique to a kind of fault tolerant neural network. Consider a neural network to be suffering from multiple-node fault, a formulae similar to that of Generalized Prediction Error has been derived. Hence, the effective number of parameter of such a fault tolerant neural network is obtained. A difficulty in obtaining the mean prediction error is discussed and then a simple procedure for estimation of the prediction error empirically is suggested.",
author = "John Sum and Chi-Sing Leung and Kevin Ho",
year = "2006",
month = oct,
doi = "10.1007/11893028_58",
language = "English",
isbn = "3540464794",
series = "Lecture Notes in Computer Science",
publisher = "Springer Verlag",
pages = "521--528",
editor = "Irwin King and Jun Wang and Laiwan Chan and DeLiang Wang",
booktitle = "Neural Information Processing",
address = "Germany",
note = "13th International Conference on Neural Information Processing (ICONIP 2006) ; Conference date: 03-10-2006 Through 06-10-2006",
}