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@inproceedings{565475, author = {Bugmann, Guido and Sojka, Petr and Reiss, Michael and Plumbley, Mark and Taylor, John G.}, address = {Brighton, UK}, booktitle = {Artificial Neural Networks II: Proceedings of the International Conference on Artificial Neural Networks ICANN 1999}, keywords = {multilayer perceptron; back propagation; robustness of neural nets}, language = {eng}, location = {Brighton, UK}, isbn = {0-444-89488-8}, pages = {1063-1066}, publisher = {Elsevier Science Publishers B.V.}, title = {Direct Approaches to Improving the Robustness of Multilayer Neural Networks}, url = {http://www.fi.muni.cz/usr/sojka/publ.html}, year = {1992} }
TY - JOUR ID - 565475 AU - Bugmann, Guido - Sojka, Petr - Reiss, Michael - Plumbley, Mark - Taylor, John G. PY - 1992 TI - Direct Approaches to Improving the Robustness of Multilayer Neural Networks PB - Elsevier Science Publishers B.V. CY - Brighton, UK SN - 0444894888 KW - multilayer perceptron KW - back propagation KW - robustness of neural nets UR - http://www.fi.muni.cz/usr/sojka/publ.html L2 - http://www.fi.muni.cz/usr/sojka/publ.html N2 - Multilayer neural networks trained with backpropagation are in general not robust against the loss of a hidden neuron. In this paper we define a form of robustness called 1-node robustness and propose methods to improve it. One approach is based on modification of the error function by the addition of a ``robustness error''. It leads to more robust networks but at the cost of a reduced accuracy. A second approach, ``pruning-and-duplication'', consists of duplicating the neurons whose loss is the most damaging for the network. Pruned neurons are used for the duplication. This procedure leads to robust and accurate networks at low computational cost. It may also prove beneficial for generalisation. ER -
BUGMANN, Guido, Petr SOJKA, Michael REISS, Mark PLUMBLEY and John G. TAYLOR. Direct Approaches to Improving the Robustness of Multilayer Neural Networks. In \textit{Artificial Neural Networks II: Proceedings of the International Conference on Artificial Neural Networks ICANN 1999}. Brighton, UK: Elsevier Science Publishers B.V., 1992, p.~1063-1066. ISBN~0-444-89488-8.
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