An algorithm for fast and reliable ESOM learning

ESANN 2006 Proceedings - European Symposium on Artificial Neural Networks

Volumn , Issue , 2006, Pages 131-136

  Nöcker, Mario ^a   Mörchen, Fabian ^a   Ultsch, Alfred ^a  

^a PHILIPPS UNIVERSITY MARBURG   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

CONFORMAL MAPPING; LARGE DATASET; SELF ORGANIZING MAPS;

BATCH LEARNING; CLUSTER STRUCTURE; DATA SPACE; EMERGENT SELF ORGANIZING MAPS; LARGE DATASETS; SEQUENTIAL LEARNING; SPEED UP;

LEARNING ALGORITHMS;

EID: 85069788587     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (10)

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2. A. Ultsch and F. Mörchen. ESOM-Maps: tools for clustering, visualization, and classification with Emergent SOM. Technical Report 46, Dept. CS, University of Marburg, Germany, 2005.
3. T. Kohonen. Self-Organizing Maps. Springer, 1995.
4. T. Kohonen, S. Kaski, K. Lagus, J. Salojrvi, J. Honkela, V. Paatero, and A. Saarela. Self organization of a massive document collection. IEEE Trans. on Neural Networks, 11(3):574-585, 2000.
5. E. Cuadros-Vargas, R.A.F. Romero, and K. Obermayer. Speeding up algorithms of SOM family for large and high dimensional databases. In Proc. WSOM, pages 167-172, 2003.
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7. A. Ultsch and L. Herrmann. The architecture of emergent self-organizing maps to reduce projection errors. In Proc. ESANN, pages 1-6, 2005.
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10. J.-C. Fort, P. Letremy, and M. Cottrell. Advantages and drawbacks of the batch kohonen algorithm. In Proc. ESANN, 2002.