DocumentCode :
1749050
Title :
Multilayer feedforward weight initialization
Author :
Hernández-Espinosa, Carlos ; Fernández-Redondo, Mercedes
Author_Institution :
Univ. Jaume I, Castellon, Spain
Volume :
1
fYear :
2001
fDate :
2001
Firstpage :
166
Abstract :
We present the results of an experimental comparison among seven different weight initialization methods in twelve different problems. The comparison is performed by measuring the speed of convergence, the generalization capability and the probability of successful convergence. It is not usual to find an evaluation of the three properties in the literature on weight initialization. The training algorithm was backpropagation with a hyperbolic tangent transfer function. We found that the performance can be improved with respect to the usual initialization scheme
Keywords :
backpropagation; convergence; feedforward neural nets; generalisation (artificial intelligence); transfer functions; backpropagation; convergence; feedforward neural network; generalization; learning algorithm; network performance; transfer function; weight initialization; Backpropagation algorithms; Bibliographies; Convergence; Equations; Neural networks; Nonhomogeneous media; Performance evaluation; Transfer functions; Velocity measurement;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks, 2001. Proceedings. IJCNN '01. International Joint Conference on
Conference_Location :
Washington, DC
ISSN :
1098-7576
Print_ISBN :
0-7803-7044-9
Type :
conf
DOI :
10.1109/IJCNN.2001.939011
Filename :
939011
Link To Document :
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