DocumentCode
2224356
Title
Dynamic pruning algorithms for improving generalisation of neural networks
Author
Babri, H.A. ; Kot, A.C. ; Tan, N.T. ; Tang, J.G.
Author_Institution
Sch. of Electr. & Electron. Eng., Nanyang Technol. Inst., Singapore
fYear
1997
fDate
9-12 Sep 1997
Firstpage
679
Abstract
In this paper, we study the effects of pruning on the generalisation performance of feed-forward networks. The weighted sensitivity method and ratio dependent sensitivity method are proposed to efficiently prune a given network. Pruning criteria using statistical methods are also discussed and experimental results are presented to illustrate their effects on the generalisation property of the network
Keywords
feedforward neural nets; generalisation (artificial intelligence); feed-forward networks; generalisation; pruning; ratio dependent sensitivity; weighted sensitivity; Cost function; Feedforward systems; Heuristic algorithms; Integral equations; Interpolation; Neural networks; Neurons; Redundancy; Signal processing algorithms; Statistical analysis;
fLanguage
English
Publisher
ieee
Conference_Titel
Information, Communications and Signal Processing, 1997. ICICS., Proceedings of 1997 International Conference on
Print_ISBN
0-7803-3676-3
Type
conf
DOI
10.1109/ICICS.1997.652063
Filename
652063
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