DocumentCode :
1649358
Title :
Neural network pruning using MV regularizer
Author :
Nagata, Takashi ; Kawata, Atsushi ; Yamada, Ken-ichi ; Nakano, Ryohei
Author_Institution :
Nagoya Inst. of Technol., Japan
Volume :
1
fYear :
2002
fDate :
6/24/1905 12:00:00 AM
Firstpage :
1051
Lastpage :
1055
Abstract :
Proposes a method of neural network pruning by using a strong regularizer called the MV regularizer. The MV regularizer learns a distinct penalty factor attached to each weight by solving a minimization problem over the validation error. After the learning, penalty factors are used to prune network weights one by one by monitoring generalization performance. Experiments using artificial data and real data showed the proposed method worked very well to remove a number of insignificant weights without the loss of generalization
Keywords :
learning (artificial intelligence); minimisation; neural nets; MV regularizer; generalization performance; minimization problem; neural network pruning; penalty factor; strong regularizer; validation error; weights pruning; Degradation; Ear; Monitoring; Multilayer perceptrons; Neodymium; Neural networks; Paper technology; Testing; Training data;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks, 2002. IJCNN '02. Proceedings of the 2002 International Joint Conference on
Conference_Location :
Honolulu, HI
ISSN :
1098-7576
Print_ISBN :
0-7803-7278-6
Type :
conf
DOI :
10.1109/IJCNN.2002.1005621
Filename :
1005621
Link To Document :
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