Name
Title | ||
|---|---|---|
New Neural Network Based Approach Helps To Discover Hidden Russian Parliament Voting Patterns |
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. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1109/IJCNN.2006.247354 | 2006 IEEE INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORK PROCEEDINGS, VOLS 1-10 |
Keywords | DocType | ISSN |
factor analysis,neural network,lyapunov function,data analysis,neural networks,principal component analysis,pattern analysis,data mining,signal analysis,voting | Conference | 2161-4393 |
Citations | PageRank | References |
7 | 0.82 | 2 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Alexander A. Frolov | 1 | 180 | 29.31 |
| Dusan Húsek | 2 | 60 | 11.37 |
| Pavel Polyakov | 3 | 29 | 3.91 |
| Hana Rezanková | 4 | 56 | 9.79 |