DocumentCode
3262874
Title
Problem and Strategy: Overfitting in Recurrent Cycles of Internal Symmetry Networks by Back Propagation
Author
Li, Guanzhong
Author_Institution
Sch. of Comput. Sci. & Eng., Univ. of New South Wales, Sydney, NSW, Australia
Volume
2
fYear
2009
fDate
6-7 June 2009
Firstpage
401
Lastpage
404
Abstract
Overfitting is an important topic in Neural Network. Internal Symmetry Networks are a new modern Cellular Neural Networks inspired by the phenomenon of internal symmetry in quantum physics. Recurrent Internal Symmetry Networks are just studied very recently. In this paper, overfitting in recurrent cycles of Internal Symmetry Networks is analyzed. Back propagation is trained for an image processing task.
Keywords
backpropagation; cellular neural nets; recurrent neural nets; back propagation neural nets; cellular neural networks; image processing task; internal symmetry networks recurrent cycles; quantum physics; Australia; Cellular neural networks; Computational intelligence; Computer networks; Computer science; Lattices; Neural networks; Physics; Recurrent neural networks; Reflection; cellular neural networks; dynamic; group representations; internal symmetry; overfitting; recurrent cycle;
fLanguage
English
Publisher
ieee
Conference_Titel
Computational Intelligence and Natural Computing, 2009. CINC '09. International Conference on
Conference_Location
Wuhan
Print_ISBN
978-0-7695-3645-3
Type
conf
DOI
10.1109/CINC.2009.258
Filename
5230942
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