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A new method to evaluate a trained artificial neural network

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conference contribution
posted on 27.01.2009, 09:33 by Yingjie Yang, Chris J. Hinde, D Gillingwater
In comparison with traditional local sample testing methods, this paper proposes a new approach to evaluate a trained neural network. A new parameter is defined to identify the different potential roles of the individual input factors based on the trained connections of the nodes in the network. Compared with field-specific knowledge, the dominance of individual input factors can be checked and then false mappings satisfying only the specific data set may be avoided.

History

School

  • Science

Department

  • Computer Science

Citation

YANG, Y., HINDE, C.J. and GILLINGWATER, D., 2001. A new method to evaluate a trained artificial neural network. IN: Proceedings. IJCNN '01. International Joint Conference Neural Networks, Washington, DC, 15-19 July, Vol.4, pp. 2620-2625

Publisher

© IEEE

Version

VoR (Version of Record)

Publication date

2001

Notes

This is a conference paper [© IEEE]. It is also available at: http://ieeexplore.ieee.org/ Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

ISBN

0780370449

Language

en

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