DocumentCode :
314403
Title :
Avoiding weight-illgrowth: cascade correlation algorithm with local regularization
Author :
Wu, Qingyao ; Nakayama, Kenji
Author_Institution :
Graduate Sch. of Nat. Sci. & Tech., Kanazawa Univ., Japan
Volume :
3
fYear :
1997
fDate :
9-12 Jun 1997
Firstpage :
1954
Abstract :
This paper investigates some possible problems of cascade correlation algorithm, one of which is the zigzag output mapping caused by weight-illgrowth of the adding hidden unit. Without doubt, it could lead to deterioration of the generalization, especially for regression problems. To solve this problem, we combine the cascade correlation algorithm with regularization theory. In addition, some new regularization terms are proposed in light of special cascade structure. Simulation has shown that regularization can smooth the zigzag output, so that the generalization is improved, especially for functional approximation
Keywords :
backpropagation; correlation methods; feedforward neural nets; function approximation; generalisation (artificial intelligence); backpropagation; cascade correlation algorithm; feedforward neural networks; functional approximation; generalisation; local regularization; weight-illgrowth; zigzag output mapping; Approximation algorithms; Backpropagation; Computer architecture; Feedforward neural networks; Multi-layer neural network; Network address translation; Network topology; Neural networks; Pattern recognition; Supervised learning;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks,1997., International Conference on
Conference_Location :
Houston, TX
Print_ISBN :
0-7803-4122-8
Type :
conf
DOI :
10.1109/ICNN.1997.614198
Filename :
614198
Link To Document :
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