Yang_Hinde_Gillingwater_IEEE_2001.pdf (596.78 kB)
A new method to evaluate a trained artificial neural network
conference contribution
posted on 2009-01-27, 09:33 authored by Yingjie Yang, Chris J. Hinde, D GillingwaterIn 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. 2620-2625Publisher
© IEEEVersion
- VoR (Version of Record)
Publication date
2001Notes
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
0780370449Language
- en