Practical assessment of neural network applications

Ian T. Nabney; Mickael J S Paven; Richard C Eldridge; Clive Lee

Practical assessment of neural network applications

Ian T. Nabney, Mickael J S Paven, Richard C Eldridge, Clive Lee

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Abstract

This paper reports the initial results of a joint research project carried out by Aston University and Lloyd's Register to develop a practical method of assessing neural network applications. A set of assessment guidelines for neural network applications were developed and tested on two applications. These case studies showed that it is practical to assess neural networks in a statistical pattern recognition framework. However there is need for more standardisation in neural network technology and a wider takeup of good development practice amongst the neural network community.

Original language	English
Title of host publication	SafeComp 97: Proceedings of the 16th International Conference on Computing Safety, Reliability and Security
Editors	Peter Daniel
Place of Publication	Germany
Publisher	Springer
Pages	357-368
Number of pages	12
ISBN (Print)	9783540761914
Publication status	Published - 1997

Bibliographical note

Copyright of Springer 16th International Conference on Computer Safety, Reliability, and Security, York, UK, September 7-10, 1997

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NCRG

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AU - Lee, Clive

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N2 - This paper reports the initial results of a joint research project carried out by Aston University and Lloyd's Register to develop a practical method of assessing neural network applications. A set of assessment guidelines for neural network applications were developed and tested on two applications. These case studies showed that it is practical to assess neural networks in a statistical pattern recognition framework. However there is need for more standardisation in neural network technology and a wider takeup of good development practice amongst the neural network community.

AB - This paper reports the initial results of a joint research project carried out by Aston University and Lloyd's Register to develop a practical method of assessing neural network applications. A set of assessment guidelines for neural network applications were developed and tested on two applications. These case studies showed that it is practical to assess neural networks in a statistical pattern recognition framework. However there is need for more standardisation in neural network technology and a wider takeup of good development practice amongst the neural network community.

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M3 - Chapter in a book

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EP - 368

BT - SafeComp 97: Proceedings of the 16th International Conference on Computing Safety, Reliability and Security

A2 - Daniel, Peter

PB - Springer

CY - Germany

ER -

Practical assessment of neural network applications

Abstract

Bibliographical note

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Handle.net

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