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Title: A new method to evaluate a trained artificial neural network
Authors: Yang, Yingjie
Hinde, Chris J.
Gillingwater, David
Keywords: Neural nets
Issue Date: 2001
Publisher: © IEEE
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
Abstract: 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.
Description: 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.
Version: Published
DOI: 10.1109/IJCNN.2001.938783
URI: https://dspace.lboro.ac.uk/2134/4122
ISBN: 0780370449
Appears in Collections:Conference Papers and Presentations (Computer Science)

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