Reliability and Convergence on Kohonen Maps: An Empirical Study

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 3213, Issue , 2004, Pages 426-433

  Adorno, Marcello Cattaneo ^a   Resta, Marina ^b  

^a CA Investment Advisers Ltd   (United Kingdom)

^b UNIVERSITY OF GENOA   (Italy)

Author keywords

[No Author keywords available]

Indexed keywords

CONFORMAL MAPPING; KNOWLEDGE BASED SYSTEMS; STATISTICAL MECHANICS;

CONVERGENCE BEHAVIORS; EMPIRICAL STUDIES; MATRIX METHODS; POINTS OF CONTACT; SPEED OF CONVERGENCE; STATE OF THE ART; STATISTICAL TOOLS; THREE DIFFERENT TECHNIQUES;

SELF ORGANIZING MAPS;

EID: 35048852569     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-30132-5_61     Document Type: Article

References (9)

1. Cattaneo Adorno, M., Resta, M.: A note on the sensitivity to parameters in the convergence of Self-Organizing Maps, in V.Palade, L. C.Jain and R.J.Hewlett, Lecture Notes in AI, LNCS/LNAI series, Springer Verlag, Heidelberg, 2003.
2. Cattaneo Adorno, M., Koho II User Manual, available for download upon request to Marina Resta (resta@economia.unige.it).
3. De Bodt, E., Cottrell, M., Verleysen, M.Statistical tools to assess the reliability of self-organizing maps, Neural Networks, 15, 967-978, (2002).
4. Gershenfeld, N., Weigend, A., Time Series Prediction: Forecasting the Future and Understanding the Past, Santa Fe Institute Studies in the Sciences of Complexity, Addison-Wesley, Reading MA (1993).
5. Kohonen, T.: Self-Organizing Maps, Springer Series in Information Sciences, Vol. 30, Springer, Berlin, Heidelberg, New York, (1997).
6. Hartigan, John A., Clustering Algorithms, John Wiley & Sons, New York. (1975)
7. Hartigan, J.A., and M.A. Wong, Algorithm AS 136: A K-means clustering algorithm, Applied Statistics, 28, 100-108, (1979).
8. Martinetz, T., Schulten, K, Topology Representing Networks, Neural Networks, vol. 7, No. 3, (1994).
9. Ultsch, A.m Vetter, C.: Self-Organizing Feature-Maps versus statistical clustering methods: a benchmark. Research Report Nr. 90194, Department of Computer Science, University of Marburg, (1994).