Abstract
We extend the self-organizing map (SOM) in the form as proposed by Heskes to a supervised fuzzy classification method. On the one hand, this leads to a robust classifier where efficient learning with fuzzy labeled or partially contradictory data is possible. On the other hand, the integration of labeling into the location of prototypes in a SOM leads to a visualization of those parts of the data relevant for the classification.
Chapter PDF
Similar content being viewed by others
References
Bauer, H.-U., Pawelzik, K.R.: Quantifying the neighborhood preservation of Self-Organizing Feature Maps. IEEE Trans. on Neural Networks 3(4), 570–579 (1992)
Brüß, C., Bollenbeck, F., Schleif, F.-M., Weschke, W., Villmann, T., Seiffert, U.: Fuzzy image segmentation with fuzzy labeled neural gas. In: Verleysen, M. (ed.) Proc. of European Symposium on Artificial Neural Networks (ESANN 2006), Brussels, Belgium, pp. 563–568. d-side publications (2006)
Hammer, B., Strickert, M., Villmann, T.: Supervised neural gas with general similarity measure. Neural Processing Letters 21(1), 21–44 (2005)
Hammer, B., Villmann, T.: Generalized relevance learning vector quantization. Neural Networks 15(8-9), 1059–1068 (2002)
Hammer, B., Villmann, T.: Classification using non-standard metrics. In: Verleysen, M. (ed.) Proc. of European Symposium on Artificial Neural Networks (ESANN 2005), Brussels, Belgium, pp. 303–316. d-side publications (2005)
Hastie, T., Stuetzle, W.: Principal curves. J. Am. Stat. Assn. 84, 502–516 (1989)
Hecht-Nielsen, R.: Counterprogagation networks. Appl. Opt. 26(23), 4979–4984 (1987)
Hecht-Nielsen, R.: Applications of counterpropagation networks. Neural Networks 1(2), 131–139 (1988)
Heskes, T.: Energy functions for self-organizing maps. In: Oja, E., Kaski, S. (eds.) Kohonen Maps, pp. 303–316. Elsevier, Amsterdam (1999)
Kohonen, T.: Self-Organizing Maps. Springer Series in Information Sciences, vol. 30. Springer, Heidelberg (1995) (Second Extended Edition 1997)
Sinkkonen, J., Kaski, S.: Clustering based on conditional distributions in an auxiliary space. Neural Computation 14, 217–239 (2002)
Villmann, T., Der, R., Herrmann, M., Martinetz, T.: Topology Preservation in Self–Organizing Feature Maps: Exact Definition and Measurement. IEEE Transactions on Neural Networks 8(2), 256–266 (1997)
Villmann, T., Hammer, B., Schleif, F.-M., Geweniger, T.: Fuzzy classification by fuzzy labeled neural gas. Neural Networks (in press, 2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Villmann, T., Seiffert, U., Schleif, FM., Brüß, C., Geweniger, T., Hammer, B. (2006). Fuzzy Labeled Self-Organizing Map with Label-Adjusted Prototypes. In: Schwenker, F., Marinai, S. (eds) Artificial Neural Networks in Pattern Recognition. ANNPR 2006. Lecture Notes in Computer Science(), vol 4087. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11829898_5
Download citation
DOI: https://doi.org/10.1007/11829898_5
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-37951-5
Online ISBN: 978-3-540-37952-2
eBook Packages: Computer ScienceComputer Science (R0)