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Volumn , Issue , 2007, Pages

Emergence in self organizing feature maps

Author keywords

Clustering; Emergence; SOM; U Matrix; Visualization

Indexed keywords

CATCHMENT BASINS; CLUSTERING; EMERGENCE; EMERGENT PROPERTY; SOM; SYNTHETIC DATA; U-MATRIX;

EID: 84893492722     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (52)

References (13)
  • 1
    • 0020068152 scopus 로고
    • Self-organized formation of topologi-cally correct feature maps
    • T. Kohonen, "Self-Organized formation of topologi-cally correct feature maps,", Biological Cybernetics, Vol. 43, pp. 59-69, 1982.
    • (1982) Biological Cybernetics , vol.43 , pp. 59-69
    • Kohonen, T.1
  • 3
    • 84893490204 scopus 로고    scopus 로고
    • The problems faced when defining emergence
    • University of Sussex, 02-06
    • K. Bentley: "The Problems Faced when Defining Emergence,", Hystory and Philosophy of Adaptive Systems, University of Sussex, 02-06, 2006.
    • (2006) Hystory and Philosophy of Adaptive Systems
    • Bentley, K.1
  • 4
    • 0002746315 scopus 로고
    • Towards a theory of emergent functionality
    • J-A, Meyer & S. W. Wilson (eds.) MIT Pres
    • L. Steels: "Towards a theory of Emergent Functionality,". In J-A, Meyer & S. W. Wilson (eds.), From Animals to Animats, MIT Pres.pp 451-461 (1991).
    • (1991) From Animals to Animats , pp. 451-461
    • Steels, L.1
  • 5
    • 24944572401 scopus 로고    scopus 로고
    • Maps for the visualization of high-dimensional data spaces
    • A. Ultsch: "Maps for the Visualization of high-dimensional Data Spaces,", Proc WSOM03, pp. 225-230, 2003.
    • (2003) Proc WSOM03 , pp. 225-230
    • Ultsch, A.1
  • 6
    • 0026172104 scopus 로고
    • Watersheds in digital spaces: An efficient algorithm based on immersion simulations
    • V. Luc, P. Soille: "Watersheds in Digital Spaces: An Efficient Algorithm Based on Immersion Simulations,", IEEE Transactions of Pattern Analysis and Machine Intelligence, Vol. 13(6), pp. 583-598, 1991.
    • (1991) IEEE Transactions of Pattern Analysis and Machine Intelligence , vol.13 , Issue.6 , pp. 583-598
    • Luc, V.1    Soille, P.2
  • 8
    • 84890308263 scopus 로고    scopus 로고
    • *F clustering: A new performant cluster-mining method based on segmentation of self-organizing maps
    • Paris, France
    • *F clustering: a new performant cluster-mining method based on segmentation of Self-Organizing Maps,", Proceedings Workshop on Self-Organizing Maps, Paris, France, pp 25-32, 2005.
    • (2005) Proceedings Workshop on Self-Organizing Maps , pp. 25-32
    • Moutarde, F.1    Ultsch, A.2
  • 10
    • 84879539152 scopus 로고    scopus 로고
    • Density estimation and visualization for data containing clusters of unknown structure
    • Weihs, C., Gaul, W. (Eds) Springer, Heidelberg
    • A. Ultsch: "Density Estimation and Visualization for Data containing Clusters of unknown Structure,", In Weihs, C., Gaul, W. (Eds), Classification; The Ubiquitous Challenge, Springer, Heidelberg, pp. 232-239, 2005.
    • (2005) Classification; the Ubiquitous Challenge , pp. 232-239
    • Ultsch, A.1
  • 11
    • 15844385952 scopus 로고    scopus 로고
    • Comparison of substructural epitopes in enzyme active sites using self-organizing maps
    • K. Kupas, A. Ultsch, G. Klebe: "Comparison of Substructural Epitopes in Enzyme Active Sites using Self-organizing Maps,", J. Comput.-Aided Mol. Design, 18, pp 697-708, 2004.
    • (2004) J. Comput.-Aided Mol. Design , vol.18 , pp. 697-708
    • Kupas, K.1    Ultsch, A.2    Klebe, G.3
  • 12
    • 33749840029 scopus 로고    scopus 로고
    • Self organizing neural networks perform different from statistical k-means clustering
    • München
    • A. Ultsch: Self Organizing Neural Networks perform different from statistical k-means clustering, BMBF Seminar KI, Neuroinformatics and intelligent systems, München, (1996), pp. 433-443
    • (1996) BMBF Seminar KI, Neuroinformatics and Intelligent Systems , pp. 433-443
    • Ultsch, A.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.