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Volumn 21, Issue 1, 2005, Pages 45-51

Improving the robustness of 'online agglomerative clustering method' based on kernel-induce distance measures

Author keywords

Competitive learning; Kernel induced measure; Nonstationary; Online clustering; Robustness

Indexed keywords

ALGORITHMS; BENCHMARKING; DATA ACQUISITION; DATA PROCESSING; INTERNET; NEURAL NETWORKS; ONLINE SYSTEMS; RADIAL BASIS FUNCTION NETWORKS;

EID: 12844281842     PISSN: 13704621     EISSN: None     Source Type: Journal    
DOI: 10.1007/s11063-004-2793-y     Document Type: Article
Times cited : (14)

References (8)
  • 2
    • 0037382210 scopus 로고    scopus 로고
    • On-line pattern analysis by evolving self-organizing maps
    • Deng, D. and Kasabov, N.: On-line pattern analysis by evolving self-organizing maps, Neurocomputing 51 (2003), 87-103.
    • (2003) Neurocomputing , vol.51 , pp. 87-103
    • Deng, D.1    Kasabov, N.2
  • 3
    • 0033556908 scopus 로고    scopus 로고
    • An on-line agglomerative clustering method for nonstationary data
    • Guedalia, I. D., London, M. and Werman, M.: An on-line agglomerative clustering method for nonstationary data, Neural Computation 11 (1999), 521-540.
    • (1999) Neural Computation , vol.11 , pp. 521-540
    • Guedalia, I.D.1    London, M.2    Werman, M.3
  • 6
    • 0347243182 scopus 로고    scopus 로고
    • Nonlinear component analysis as a kernel eigenvalue problem
    • Schölkopf, B., Smola, A. and Müller, K. R.: Nonlinear component analysis as a kernel eigenvalue problem, Neural Computation 10(5) (1998), 1299-1319.
    • (1998) Neural Computation , vol.10 , Issue.5 , pp. 1299-1319
    • Schölkopf, B.1    Smola, A.2    Müller, K.R.3
  • 7
    • 0028607656 scopus 로고
    • A new competitive learning approach based on an equidistortion principle for designing optimal vector quantizers
    • Ueda, N. and Nakano, R.: A new competitive learning approach based on an equidistortion principle for designing optimal vector quantizers, Neural Networks 7(8) (1994), 1211-1227.
    • (1994) Neural Networks , vol.7 , Issue.8 , pp. 1211-1227
    • Ueda, N.1    Nakano, R.2
  • 8
    • 0842300534 scopus 로고    scopus 로고
    • Clustering incomplete data using kernel-based fuzzy c-means algorithm
    • Zhang, D. and Chen, S.: Clustering incomplete data using kernel-based fuzzy c-means algorithm, Neural Processing Letters 18 (2003), 155-162.
    • (2003) Neural Processing Letters , vol.18 , pp. 155-162
    • Zhang, D.1    Chen, S.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.