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Weight Groupings in the Training of Recurrent Networks
Como, Italy July 24-July 27
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/IJCNN.2000.861275IEEE-INNS-ENNS International Joint Co ...
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Lai-Wan Chan, Chi-Cheong Szeto, "Weight Groupings in the Training of Recurrent Networks," Neural Networks, IEEE - INNS - ENNS International Joint Conference on, vol. 3, pp. 3021, IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 3, 2000.
 
 
BibTex
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@article{ 10.1109/IJCNN.2000.861275,
author = {Lai-Wan Chan and Chi-Cheong Szeto},
title = {Weight Groupings in the Training of Recurrent Networks},
journal ={Neural Networks, IEEE - INNS - ENNS International Joint Conference on},
volume = {3},
year = {2000},
issn = {1098-7576},
pages = {3021},
doi = {http://doi.ieeecomputersociety.org/10.1109/IJCNN.2000.861275},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
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TY - CONF
JO - Neural Networks, IEEE - INNS - ENNS International Joint Conference on
TI - Weight Groupings in the Training of Recurrent Networks
SN - 1098-7576
SP
EP
A1 - Lai-Wan Chan,
A1 - Chi-Cheong Szeto,
PY - 2000
VL - 3
JA - Neural Networks, IEEE - INNS - ENNS International Joint Conference on
ER -
 
Lai-Wan Chan, Chinese University of Hong Kong
Chi-Cheong Szeto, Chinese University of Hong Kong
In this paper, we use the block-diagonal matrix to approximate the Hessian matrix in the Levenberg Marquardt method in the training of recurrent neural networks. Substantial improvement of the training time over the original Levenberg Marquardt method is observed without degrading the generalization ability. Three weight grouping methods, correlation blocks, k-unit blocks and layer blocks were investigated and compared. Their computational complexity, approximation ability, and training time are analyzed.
Citation:
Lai-Wan Chan, Chi-Cheong Szeto, "Weight Groupings in the Training of Recurrent Networks," ijcnn, vol. 3, pp.3021, IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 3, 2000
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