Abstract
Self-organizing maps are closely related to radial basis function networks. They can be seen as radial basis function networks without an output layer, or, rather, the hidden layer of a radial basis function network is already the output layer of a self-organizing map. This output layer also has an internal structure, since the neurons are arranged in a grid. The neighborhood relationships resulting from this grid are exploited in the training process in order to determine a topology preserving map.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
Note that the distance is computed from the grid, in which the output neurons are arranged, and not from the position of the reference vectors or the distance measure in the input space.
References
F. Aurenhammer. Voronoi Diagrams—A Survey of a Fundamental Geometric Data Structure. ACM Computing Surveys 23(3):345–405. ACM Press, New York, NY, USA, 1991
T. Kohonen. Self-organized Formation of Topologically Correct Feature Maps. Biological Cybernetics. Springer-Verlag, Heidelberg, Germany, 1982
T. Kohonen. Learning Vector Quantization for Pattern Recognition. Technical Report TKK-F-A601. Helsinki University of Technology, Finland, 1986
T. Kohonen. Improved Versions of Learning Vector Quantization. Proc. Int. Joint Conference on Neural Networks (IJCNN 1990, San Diego, CA), 1:545–550. IEEE Press, Piscataway, NJ, USA, 1990
T. Kohonen. Self-Organizing Maps. Springer-Verlag, Heidelberg, Germany, 1995 (3rd ext. edition 2001)
S. Seo and K. Obermayer. Soft Learning Vector Quantization. Neural Computation 15(7):1589–1604. MIT Press, Cambridge, MA, USA, 2003
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag London
About this chapter
Cite this chapter
Kruse, R., Borgelt, C., Klawonn, F., Moewes, C., Steinbrecher, M., Held, P. (2013). Self-organizing Maps. In: Computational Intelligence. Texts in Computer Science. Springer, London. https://doi.org/10.1007/978-1-4471-5013-8_7
Download citation
DOI: https://doi.org/10.1007/978-1-4471-5013-8_7
Publisher Name: Springer, London
Print ISBN: 978-1-4471-5012-1
Online ISBN: 978-1-4471-5013-8
eBook Packages: Computer ScienceComputer Science (R0)