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Volumn , Issue , 2010, Pages 481-486

Kernel generative topographic mapping

Author keywords

[No Author keywords available]

Indexed keywords

GENERATIVE TOPOGRAPHIC MAPPING; IDENTIFICATION OF PROTEINS; MANIFOLD LEARNING; PROTEIN SEQUENCES;

EID: 79953062676     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (22)

References (11)
  • 1
    • 0347963789 scopus 로고    scopus 로고
    • GTM: The Generative Topographic Mapping
    • Elsevier
    • C. M. Bishop, M. Svensén, and C. K. I. Williams, GTM: The Generative Topographic Mapping, Neural Comput., 10(1):215-234, Elsevier, 1998.
    • (1998) Neural Comput , vol.10 , Issue.1 , pp. 215-234
    • Bishop, C.M.1    Svensén, M.2    Williams, C.K.I.3
  • 3
    • 51049101496 scopus 로고    scopus 로고
    • Advances in clustering and visualization of time series using GTM Through Time
    • Elsevier
    • I. Olier and A. Vellido. Advances in clustering and visualization of time series using GTM Through Time. Neural Networks, 21(7):904-913, Elsevier, 2008.
    • (2008) Neural Networks , vol.21 , Issue.7 , pp. 904-913
    • Olier, I.1    Vellido, A.2
  • 4
    • 0036825785 scopus 로고    scopus 로고
    • Latent variable models for the topographic organisation of discrete and strictly positive data
    • Elsevier
    • M. Girolami, Latent variable models for the topographic organisation of discrete and strictly positive data, Neurocomp., 48:185-198, Elsevier, 2002.
    • (2002) Neurocomp , vol.48 , pp. 185-198
    • Girolami, M.1
  • 5
    • 0347243182 scopus 로고    scopus 로고
    • Nonlinear component analysis as a kernel eigenvalue problem
    • MIT Press
    • B. Schölkopf, A. Smola, and K. R. Muller, Nonlinear component analysis as a kernel eigenvalue problem, Neural Comput., 10(5):1299-1319, MIT Press, 1998.
    • (1998) Neural Comput , vol.10 , Issue.5 , pp. 1299-1319
    • Schölkopf, B.1    Smola, A.2    Muller, K.R.3
  • 7
    • 84893464084 scopus 로고    scopus 로고
    • A comparison between dissimilarity SOM and kernel SOM for clustering the vertices of a graph
    • Bielefield, Germany
    • N. Villa and F. Rossi, A comparison between dissimilarity SOM and kernel SOM for clustering the vertices of a graph. In proceedings of the 6th Workshop on Self-Organizing Maps (WSOM 07), Bielefield, Germany, 2007.
    • (2007) Proceedings of the 6th Workshop On Self-Organizing Maps (WSOM 07)
    • Villa, N.1    Rossi, F.2
  • 8
    • 0002629270 scopus 로고
    • Maximum likelihood from incomplete data via the EM algorithm
    • A. P. Dempster, M. N. Laird and D. B. Rubin, Maximum likelihood from incomplete data via the EM algorithm, J. Roy. Stat. Soc. B, 39:1-38, 1977.
    • (1977) J. Roy. Stat. Soc. B , vol.39 , pp. 1-38
    • Dempster, A.P.1    Laird, M.N.2    Rubin, D.B.3
  • 9
    • 84887010308 scopus 로고    scopus 로고
    • Learning with Kernels. The MIT Press, Cambridge, Massachussets
    • B. Schölkopf and A. Smola. Learning with Kernels. The MIT Press, Cambridge, Massachussets, 2002.
    • (2002)
    • Schölkopf, B.1    Smola, A.2
  • 10
    • 84887009962 scopus 로고    scopus 로고
    • Biological sequence analysis: Probabilistic models of proteins and nucleic acids. Cambridge Univ. Press, Cambridge
    • R. Durbin, S. R. Eddy, A. Krogh, and G. Mitchison. Biological sequence analysis: Probabilistic models of proteins and nucleic acids. Cambridge Univ. Press, Cambridge, 2004.
    • (2004)
    • Durbin, R.1    Eddy, S.R.2    Krogh, A.3    Mitchison, G.4
  • 11
    • 13844272392 scopus 로고    scopus 로고
    • A novel neural network method in mining molecular sequence data
    • Z. R. Yang and R. Thomson, A novel neural network method in mining molecular sequence data, IEEE T. Neural Networ., 16:263-274, 2005.
    • (2005) IEEE T. Neural Networ , vol.16 , pp. 263-274
    • Yang, Z.R.1    Thomson, R.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.