+44 (0)1509 263171
Please use this identifier to cite or link to this item:
|Title: ||A new method to evaluate a trained artificial neural network|
|Authors: ||Yang, Yingjie|
Hinde, Chris J.
|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.|
|Appears in Collections:||Conference Papers and Presentations (Computer Science)|
Files associated with this item:
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.