DocumentCode
2774884
Title
New Neural Network Based Approach Helps to Discover Hidden Russian Parliament Voting Patterns
Author
Frolov, Alexander A. ; Husek, Dusan ; Polyakov, Pavel ; Rezankova, Hana
Author_Institution
Institute of Higher Nervous Activity and Neurophysiology of the Russian Academy of Sciences. email: aafrolov@mail.ru
fYear
2006
fDate
16-21 July 2006
Firstpage
3486
Lastpage
3491
Abstract
The sparse encoded Hopfield like neural network is modified to provide the Boolean factor analysis. New, more efficient method of sequential factor extraction, based on the characteristics behavior of the Lyapunov function is introduced. Efficiency of this attempt is shown not only on simulated data but on real data from Russian parliament but as well.
Keywords
Data analysis; Data mining; Hopfield neural networks; Lyapunov method; Neural networks; Pattern analysis; Principal component analysis; Signal analysis; Signal mapping; Voting;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 2006. IJCNN '06. International Joint Conference on
Print_ISBN
0-7803-9490-9
Type
conf
DOI
10.1109/IJCNN.2006.247354
Filename
1716576
Link To Document