Investigating learning parameters in a standard 2-D SOM model to select good maps and avoid poor ones

Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)

Volumn 3339, Issue , 2004, Pages 425-437

  Tan, Hiong Sen ^a   George, Susan E ^a  

^a UNIVERSITY OF SOUTH AUSTRALIA   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER SIMULATION; DATA REDUCTION; DATABASE SYSTEMS; MAPS; NEURAL NETWORKS;

DATA SETS; LEARNING PARAMETERS; REAL WORLD APPLICATIONS; SELF ORGANIZING MAP (SOM);

LEARNING SYSTEMS;

EID: 22944461416     PISSN: 03029743     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1007/978-3-540-30549-1_38     Document Type: Conference Paper

References (16)

1. Kohonen, T., Self-organized formation of topologically correct feature maps. Biological Cybernetics, 1982. 43: p. 59-69.
2. Kohonen, T., Self-organizing map. Second Edition ed. Springer Series in Information Sciences, ed. T.S. Huang, T. Kohonen, and M.R. Schroeder. 1995, Berlin Heidelberg: Springer-Verlag.
3. Fritzke, B., Growing cell structures - a self-organizing network for unsupervised and supervised learning. Neural Networks, 1994. 7(9): p. 1441-1460.
4. Alahakoon, D., S.K. Halgamuge, and B. Srinivasan, Dynamic self-organizing maps with controlled growth for knowledge discovery. IEEE Transactions on Neural Networks, 2000. 11(3): p. 601-614.
5. Koikkalainen, P. and K. Oja. Self-organizing hierarchical feature maps. in Proceedings of International Joint Conference on Neural Networks. 1990. San Diego, CA.
6. Cuadros-Vargas, E. and R.A.F. Romero. A SAM-SOM family: incorporating spatial access methods into constructive self-organizing maps. in Proceedings of International Joint Conference on Neural Networks. 2002. Honolulu, HI.
7. Haese, K. and G.J. Goodhill, Auto-SOM: recursive parameter estimation for guidance of self-organizing feature maps. Neural Computation, 2001. 13: p. 595-619.
8. Berglund, E. and J. Sitte. The parameter-less SOM algorithm. in Proceedings of the 8th Australian and New Zealand Intelligent Information System Conference (ANZIIS 2003). 2003. Sydney, Australia: Queensland University of Technology.
9. Lippmann, R.P., An introduction to computing with neural nets. IEEE Acoustics, Speech, and Signal Processing Society Magazine, 1987. 4(2): p. 4-22.
10. Freeman, J.A. and D.M. Skapura, Neural networks: algorithms, applications and programming techniques. 1991, Reading, Massachussets: Addison-Wesley.
11. Callan, R., The essence of neural networks. Essence of Computing. 1999, London: Prentice Hall Europe.
12. Bauer, H.-U. and K.R. Pawelzik, Quantifying the neighbourhood preservation of self-organizing feature maps. IEEE Transactions on Neural Networks, 1992. 3(4): p. 570-579.
13. Villmann, T., R. Der, and T.M. Martinetz. A new quantitative measure of topology preservation in Kohonen's feature maps. in Proceedings of IEEE International Conference on Neural Networks. 1994: IEEE.
14. Kiviluoto, K. Topology preservation in self-organizing maps. in The Proceedings of IEEE International Conference on Neural Networks. 1996. Washington, DC: IEEE.
15. Kaski, S. and K. Lagus. Comparing self-organizing maps. in Proceedings of International Conference on Artificial Neural Networks. 1996. Bochum, Germany: Springer-Verlag.
16. Varfis, A. and C. Versino, Selecting reliable Kohonen maps for data analysis. Artificial Neural Networks, 1992. 2: p. 1583-1586.