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Overture

Published:01 June 2001Publication History

ABSTRACT

An introduction to and overview of the Self-Organizing Map (SOM) methods is presented in this chapter.

References

  1. Kohonen, T. (1982) Self-organizing formation of topologically correct feature maps. Biol. Cybern. 43,59-69Google ScholarGoogle ScholarCross RefCross Ref
  2. Kohonen, T. (1982) Clustering, taxonomy, and topological maps of patterns. Proc. 6th Int. Conf. Pattern Recognition, Munich, Germany, 114-128Google ScholarGoogle Scholar
  3. Kohonen, T. (2001) Self-Organizing Maps, 3rd ed. Springer, London Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Young, G., Householder, A. S. (1938) Discussion of a set of points in terms of their mutual distances. Psychometrika 3, 19-22Google ScholarGoogle ScholarCross RefCross Ref
  5. Kruskal, 3. B., Wish, M. (1978) Multidimensional Scaling. Sage University Paper Series on Quantitative Applications in the Social Sciences No. 07-011. Sage Publications, Newbury ParkGoogle ScholarGoogle Scholar
  6. Ultsch, A., Siemon, H. (1989) Exploratory Data Analysis: Using Kohonen's Topology Preserving Maps. Technical Report 329. Univ. of Dortmund, Dortmund, GermanyGoogle ScholarGoogle Scholar
  7. Kraaijveld, M. A., Mao, J., Jam, A. K. (1992) A non-linear projection method based on Kohonen's topology preserving maps. Proc. 11ICPR, Int. Conf. on Pattern Recognition. IEEE Comput. Soc. Press, Los Alamitos, CA, 41-45. Also IEEE Trans. on Neural Networks 6, 548559 (1995)Google ScholarGoogle ScholarCross RefCross Ref
  8. Cottrell, M., Fort, 3. C., Pags, 0. (1997) Theoretical aspects of the SOM algorithm. Proc. WSOM'97, Workshop on Self-Organizing Maps. Helsinki University of Technology, Neural Networks Research Centre, Espoo, Finland, 246-267Google ScholarGoogle Scholar
  9. Cheng, Y. (1997) Convergence and ordering of Kohonen's batch map. Neural Computation 9, 1667-1676 Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. Cottrell, M., Fort, J.-C. (1987) tude d'un processus d'auto-organisation. Annales de l'lnstitut Henri Poincar 23, 1-20Google ScholarGoogle Scholar
  11. Flanagan, J. A. (1994) Self-Organizing Neural Networks. Ph.D. Thesis, Swiss Federal Inst. of Tech. Lausanne (EPFL)Google ScholarGoogle Scholar
  12. Ritter, H., Martinetz, T., Schulten, K. (1992) Neural Computation and Self-Organizing Maps: An Introduction. Addison-Wesley, Reading, MA Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. Heskes, T. (1993) Guaranteed convergence of learning rules. Proc. Int. Conf. on Artificial Neural Networks (ICANN'93), Amsterdam, The Netherlands, 533-536Google ScholarGoogle Scholar
  14. Heskes, T. and Kappen, B. (1993) Error potential for self-organization. Proc. Int. Conf. Neural Networks (ICNN'93), vol. III, 1219-1223Google ScholarGoogle ScholarCross RefCross Ref
  15. Luttrell, S. P. (1992) Code Vector Density in Topographic Mappings. Memorandum 4669. Defense Research Agency, Malvern, UKGoogle ScholarGoogle Scholar
  16. Zrehen, S. (1993) Analyzing Kohonen maps with geometry. Proc. Int. Conf. on Artificial Neural Networks (ICANN'93), Amsterdam. The Netherlands, 609-612Google ScholarGoogle ScholarCross RefCross Ref
  17. Villmann, T., Der, R., Martinetz, T. (1994) A new quantitative measure of topology preservation in Kohonen's feature maps. Proc. IEEE Int. Conf. on Neural Networks (ICNN'94), 645-648Google ScholarGoogle ScholarCross RefCross Ref
  18. Kiviluoto, K. (1996) Topology preservation in self-organizing maps. Proc. IEEE Int. Conf. on Neural Networks (ICNN'96), 294-299Google ScholarGoogle ScholarCross RefCross Ref
  19. Kaski, S., Lagus, K. (1996) Comparing self-organizing maps. Proc. Int. Confi on Artificial Neural Networks (ICANN'96), 809-814 Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. Ritter, H. (1991) Asymptotic level density for a class of vector quantization processes. IEEE Trans. Neural Networks 2, 173-175Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. Kohonen, T. (1999) Comparison of SOM point densities based on different criteria. Neural Computation 11, 2081-2095 Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. Gersho, A. (1979) Asymptotically optimal block quantization. IEEE Trans. Inform. Theory IT-25, 373-380Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. Zador, P. (1982) Asymptotic quantization error of continuous signals and the quantization dimension. IEEE Trans. Inform. Theory IT-28, 139-149Google ScholarGoogle ScholarCross RefCross Ref
  24. Kohonen, T. (1998) Computation of VQ and SOM Point Densities Using the Calculus of Variations. Report A52, Helsinki University of Technology, Laboratory of Computer and Information Science, Espoo, Finland.Google ScholarGoogle Scholar
  25. Kohonen, T., Kaski, S., Lagus, K., Salojrvi, J., Flonkela., J., Paatero, V., Saarela, A. (2000) Self-organization of a massive document collection. IEEE Trans. Neural Networks 11, 574-585 Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. Kohonen, T. (1993) Physiological interpretation of the self-organizing map algorithm. Neural Networks 6. 895-905 Google ScholarGoogle ScholarDigital LibraryDigital Library
  27. Kaski, S., Kangas, J., Kohonen, T. (1998) Bibliography of self-organizing map (SOM) papers: 1981-1997. Neural Computing Surveys 1, 1-176 (http://www.icsi.berkeley.edu/~jagota/NCS/)Google ScholarGoogle Scholar
  28. Alhoniemi, E., Hollmn, J., Simula, 0., Vesanto, J. (1999) Process monitoring and modeling using the self-organizing map. Integrated Computer-Aided Engineering 6, 3-14 Google ScholarGoogle ScholarDigital LibraryDigital Library
  29. Deboeck, 0., Kohonen T. (Eds.) (1998) Visual Exploration in Finance with Self-Organizing Maps. Springer, London (Japanese translation: Springer. Tokyo, 1999) Google ScholarGoogle ScholarDigital LibraryDigital Library
  30. Naim, A., Ratnatunga, K. U., Griffiths, R. E. (1997) Galaxy morphology without classification: self-organizing maps. Astrophys. J. Suppl. Series 111, 357-367Google ScholarGoogle ScholarCross RefCross Ref
  31. Miikkulainen, R. (1993) Subsymbolic Natural Language Processing: An Integrated Model of Scripts, Lexicon, and Memory. MIT Press, Cambridge Google ScholarGoogle ScholarDigital LibraryDigital Library
  32. Tokutaka, H., Kishida, S., Fujimura, K. (1999) Applications of Self-Organizing Maps. Kaibundo, Tokyo, Japan (in Japanese)Google ScholarGoogle Scholar
  33. van Hulle, M. M. (2000) Faithful Representations and Topographic MapsFrom Distortionto Information-Based Self-Organization. John Wiley, New York Google ScholarGoogle ScholarDigital LibraryDigital Library
  34. Oja, E., Kaski, S. (Eds.) (1999) Kohonen Maps. Elsevier, Amsterdam Google ScholarGoogle ScholarDigital LibraryDigital Library
  35. Neurocomputing 21, Special issue on self-organizing maps, Nos. 1-3, October 1998Google ScholarGoogle Scholar

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  1. Overture

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        • Published in

          cover image Guide books
          Self-Organizing neural networks: recent advances and applications
          June 2001
          292 pages
          ISBN:3790814172

          Publisher

          Springer-Verlag

          Berlin, Heidelberg

          Publication History

          • Published: 1 June 2001

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