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
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;
Conference_Titel :
Neural Networks, 2006. IJCNN '06. International Joint Conference on
Print_ISBN :
0-7803-9490-9
DOI :
10.1109/IJCNN.2006.247354
