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Volumn 28, Issue 11, 2006, Pages 1869-1874

Dynamic characterization of cluster structures for robust and inductive support vector clustering

Author keywords

Clustering; Dynamical systems; Inductive learning; Kernel methods; Support vector machines

Indexed keywords

CLUSTER ANALYSIS; CLUSTERING ALGORITHMS; COMPUTER SIMULATION; DYNAMICAL SYSTEMS; GRAPH THEORY; LEARNING SYSTEMS;

EID: 33947107389     PISSN: 01628828     EISSN: None     Source Type: Journal    
DOI: 10.1109/TPAMI.2006.225     Document Type: Article
Times cited : (143)

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    • Lee, J.1    Chiang, H.-D.2
  • 6
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    • An Improved Cluster Labeling Method for Support Vector Clustering
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    • J. Lee and D. Lee, "An Improved Cluster Labeling Method for Support Vector Clustering," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 27, no. 3, pp. 461-464, Mar. 2005.
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    • Lee, J.1    Lee, D.2
  • 8
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    • An Overviewof Statistical Learning Theory
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    • The Laplacian PDF Distance: A Cost Function for Clustering in a Kernel Feature Space
    • Cambridge, Mass, MIT Press
    • R. Jenscn, D. Erdogmus, J.C. Principe, and T. Eltoft, "The Laplacian PDF Distance: A Cost Function for Clustering in a Kernel Feature Space," Advances in Neural Information Processing Systems (NIPS), vol. 17, pp. 625-632, Cambridge, Mass.: MIT Press, 2005.
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    • Jenscn, R.1    Erdogmus, D.2    Principe, J.C.3    Eltoft, T.4
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    • Mercer Kernel Based Clustering in Feature Space
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    • M. Girolami, "Mercer Kernel Based Clustering in Feature Space," IEEE Trans. Neural Networks, vol. 13, no. 4, pp. 780-784, July 2002.
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    • Girolami, M.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.