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Volumn 4881 LNCS, Issue , 2007, Pages 77-86

Variational GTM

Author keywords

[No Author keywords available]

Indexed keywords

CLUSTERING ALGORITHMS; DATA STRUCTURES; DATA VISUALIZATION; GAUSSIAN DISTRIBUTION; MAXIMUM LIKELIHOOD; PARAMETER ESTIMATION; TOPOGRAPHY;

EID: 38449091101     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-77226-2_9     Document Type: Conference Paper
Times cited : (2)

References (13)
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    • 0347963789 scopus 로고    scopus 로고
    • GTM: The Generative Topographic Mapping
    • Bishop, C.M., Svensén, M., Williams, C.K.I.: GTM: The Generative Topographic Mapping. Neural Comput. 10(1), 215-234 (1998)
    • (1998) Neural Comput , vol.10 , Issue.1 , pp. 215-234
    • Bishop, C.M.1    Svensén, M.2    Williams, C.K.I.3
  • 3
    • 0344110441 scopus 로고    scopus 로고
    • Developments of the Generative Topographic Mapping
    • Bishop, C.M., Svensén, M., Williams, C.K.I.: Developments of the Generative Topographic Mapping. Neurocomputing 21(1-3), 203-224 (1998)
    • (1998) Neurocomputing , vol.21 , Issue.1-3 , pp. 203-224
    • Bishop, C.M.1    Svensén, M.2    Williams, C.K.I.3
  • 4
    • 0041524001 scopus 로고    scopus 로고
    • Selective smoothing of the Generative Topographic Mapping
    • Vellido, A., El-Deredy, W., Lisboa, P.J.G.: Selective smoothing of the Generative Topographic Mapping. IEEE T. Neural Networ. 14(4), 847-852 (2003)
    • (2003) IEEE T. Neural Networ , vol.14 , Issue.4 , pp. 847-852
    • Vellido, A.1    El-Deredy, W.2    Lisboa, P.J.G.3
  • 5
    • 3543081155 scopus 로고    scopus 로고
    • Variational algorithms for approximate Bayesian inference
    • PhD thesis, The Gatsby Computational Neuroscience Unit, Univ. College London
    • Beal, M.: Variational algorithms for approximate Bayesian inference. PhD thesis, The Gatsby Computational Neuroscience Unit, Univ. College London (2003)
    • (2003)
    • Beal, M.1
  • 6
    • 0042685161 scopus 로고    scopus 로고
    • Bayesian parameter estimation via variational methods
    • Jakkola, T., Jordan, M.I.: Bayesian parameter estimation via variational methods. Stat. Comput. 10, 25-33 (2000)
    • (2000) Stat. Comput , vol.10 , pp. 25-33
    • Jakkola, T.1    Jordan, M.I.2
  • 7
    • 0003501215 scopus 로고    scopus 로고
    • A review of Gaussian random fields and correlation functions
    • Technical Report 917, Norwegian Computing Center, Oslo, Norway
    • Abrahamsen, P.: A review of Gaussian random fields and correlation functions. Technical Report 917, Norwegian Computing Center, Oslo, Norway (1997)
    • (1997)
    • Abrahamsen, P.1
  • 8
    • 0034504355 scopus 로고    scopus 로고
    • Bayesian sampling and ensemble learning in Generative Topographic Mapping
    • Utsugi, A.: Bayesian sampling and ensemble learning in Generative Topographic Mapping. Neural Process. Lett. 12, 277-290 (2000)
    • (2000) Neural Process. Lett , vol.12 , pp. 277-290
    • Utsugi, A.1
  • 10
    • 0002704818 scopus 로고
    • A practical Bayesian framework for back-propagation networks
    • MacKay, D.J.C.: A practical Bayesian framework for back-propagation networks. Neural Comput. 4(3), 448-472 (1992)
    • (1992) Neural Comput , vol.4 , Issue.3 , pp. 448-472
    • MacKay, D.J.C.1
  • 12
    • 38449102410 scopus 로고    scopus 로고
    • A variational Bayesian formulation for GTM: Theoretical foundations
    • Technical report, Technical University of Catalonia UPC
    • Olier, I., Vellido, A.: A variational Bayesian formulation for GTM: Theoretical foundations. Technical report, Technical University of Catalonia (UPC) (2007)
    • (2007)
    • Olier, I.1    Vellido, A.2
  • 13
    • 4043136951 scopus 로고    scopus 로고
    • Variational Gaussian process classifiers
    • Gibbs, M., MacKay, D.J.C: Variational Gaussian process classifiers. IEEE T. Neural Networ. 11(6), 1458-1464 (2000)
    • (2000) IEEE T. Neural Networ , vol.11 , Issue.6 , pp. 1458-1464
    • Gibbs, M.1    MacKay, D.J.C.2


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