Abstract
Self-Organizing Maps (SOM) is a powerful tool for clustering and discovering patterns in data. Competitive learning in the SOM training process focuses on finding a neuron that is most similar to that of an input vector. Since an update of a neuron only benefits part of the feature map, it can be thought of as a local optimization problem. The ability to move away from a local optimization model into a global optimization model requires the use of game theory techniques to analyze overall quality of the SOM. A new algorithm GTSOM is introduced to take into account cluster quality measurements and dynamically modify learning rates to ensure improved quality through successive iterations.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Kohonen, T.: Automatic formation of topological maps of patterns in a self-organizing system. In: Proceedings of the Scandinavian Conference on Image Analysis, pp. 214–220 (1981)
Huntsberger, T., Ajjimarangsee, P.: Parallel self-organizing feature maps for unsupervised pattern recognition. International Journal of General Systems 16(4), 357–372 (1990)
Tsao, E., Lin, W., Chen, C.: Constraint satisfaction neural networks for image recognition. Pattern Recognition 26(4), 553–567 (1993)
Cho, S.B.: Ensemble of structure-adaptive self-organizing maps for high performance classification. Inf. Sci. 123(1-2), 103–114 (2000)
Hung, C., Wermter, S.: A dynamic adaptive self-organising hybrid model for text clustering. In: Proceedings of the Third IEEE International Conference on Data Mining, pp. 75–82 (2003)
Hagan, M.T., Demuth, H.B., Beale, M.H.: Neural Network Design. PWS Publishing Company, Boston (1996)
Brachman, R.J., Anand, T.: The process of knowledge discovery in databases: A human-centered approach. In: Advances in knowledge discovery and data mining, American Association for Artificial Intelligence, pp. 37–58 (1996)
Fayyad, U.M., Piatetsky-Shapiro, G., Smyth, P.: From data mining to knowledge discovery: an overview. In: Advances in knowledge discovery and data mining, American Association for Artificial Intelligence, pp. 1–34 (1996)
Chandrasekaran, V., Liu, Z.Q.: Topology constraint free fuzzy gated neural networks for pattern recognition. IEEE Transactions on Neural Networks 9(3), 483–502 (1998)
Pal, S., Dasgupta, B., Mitra, P.: Rough self organizing map. Applied Intelligence 21(3), 289–299 (2004)
Fritzke, B.: Some competitive learning methods. Technical report, Institute for Neural Computation. Ruhr-Universit at Bochum (1997)
Santini, S., Jain, R.: Similarity measures. IEEE Transactions: Pattern Analysis and Machine Intelligence 21(9), 871–883 (1999)
Kolen, J.F., Pollack, J.B.: Back propagation is sensitive to initial conditions. In: Advances in Neural Information Processing Systems, vol. 3, pp. 860–867 (1991)
von Neumann, J., Morgenstern, O.: Theory of Games and Economic Behavior. Princeton University Press, Princeton (1944)
Nash, J.: The bargaining problem. Econometrica 18(2), 155–162 (1950)
Roth, A.: The evolution of the labor market for medical interns and residents: a case study in game theory. Political Economy 92, 991–1016 (1984)
Bell, M.: The use of game theory to measure the vulnerability of stochastic networks. IEEE Transactions on Reliability 52(1), 63–68 (2003)
Fischer, J., Wright, R.N.: An application of game-theoretic techniques to cryptography. Discrete Mathematics and Theoretical Computer Science 13, 99–118 (1993)
Gossner, O.: Repeated games played by cryptographically sophisticated players. Technical report, Catholique de Louvain - Center for Operations Research and Economics (1998)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Herbert, J., Yao, J. (2005). A Game-Theoretic Approach to Competitive Learning in Self-Organizing Maps. In: Wang, L., Chen, K., Ong, Y.S. (eds) Advances in Natural Computation. ICNC 2005. Lecture Notes in Computer Science, vol 3610. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11539087_15
Download citation
DOI: https://doi.org/10.1007/11539087_15
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28323-2
Online ISBN: 978-3-540-31853-8
eBook Packages: Computer ScienceComputer Science (R0)