Abstract
The performance of Self Organizing Map (SOM) is always influenced by learn methods. The resultant quality of the topological formation of the SOM is also highly dependent onto the learning rate and the neighborhood function. In literature, there are plenty of studies to find a proper method to improve the quality of SOM. However, a new term “stiffness factor” has been proposed and was used in SOM training in this paper. The effect of the stiffness factor has also been tested with a real-world problem and got positive influence.
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References
Kohonen, T.: The Self Organizing Map. Proc. of IEEE 78(9), 1464–1480 (1990)
Kohonen, T.: Self-Organizing Maps. Springer, Heidelberg (1995)
Mulier, F.M., Cherkassky, V.: Statistical Analyses of Self-organization. Neural Networks 8(5), 717–727 (1995)
Flanagan, J.A.: Self-organization in Kohonen’s SOM. Neural Networks 9(7), 1185–1197 (1996)
Germen, E., Bilgen, S.: A Statistical Approach to Determine the Neighborhood Function and Learning Rate in Self-Organizing Maps. In: Proc. ICONIP 1997, pp. 334–337. Springer, Heidelberg (1997)
Germen, E.: Statistical Self-Organizing Map, Ph. D. Thesis, METU (1999)
Germen, E.: Increasing the Topological Quality of Kohonen’s Self Organizing Map by Using a Hit Term. In: Proc. ICONIP 2002, Singapore (2002)
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Germen, E. (2005). Improving the Resultant Quality of Kohonen’s Self Organizing Map Using Stiffness Factor. 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_43
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DOI: https://doi.org/10.1007/11539087_43
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28323-2
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