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Volumn , Issue , 2010, Pages

Online learning for Latent Dirichlet Allocation

Author keywords

[No Author keywords available]

Indexed keywords

BARIUM COMPOUNDS; E-LEARNING; STATISTICS;

EID: 85162005069     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (1284)

References (25)
  • 5
    • 79955691941 scopus 로고    scopus 로고
    • Parallel inference for latent Dirichlet allocation on graphics processing units
    • Feng Yan, Ningyi Xu, and Yuan Qi. Parallel inference for latent Dirichlet allocation on graphics processing units. In Advances in Neural Information Processing Systems 22, pages 2134-2142, 2009.
    • (2009) Advances in Neural Information Processing Systems , vol.22 , pp. 2134-2142
    • Yan, F.1    Xu, N.2    Qi, Y.3
  • 12
    • 0033225865 scopus 로고    scopus 로고
    • Introduction to variational methods for graphical models
    • M. Jordan, Z. Ghahramani, T. Jaakkola, and L. Saul. Introduction to variational methods for graphical models. Machine Learning, 37:183-233, 1999.
    • (1999) Machine Learning , vol.37 , pp. 183-233
    • Jordan, M.1    Ghahramani, Z.2    Jaakkola, T.3    Saul, L.4
  • 15
    • 17444399859 scopus 로고    scopus 로고
    • Stochastic approximations and efficient learning
    • Second edition. The MIT Press, Cambridge, MA
    • L. Bottou and N. Murata. Stochastic approximations and efficient learning. The Handbook of Brain Theory and Neural Networks, Second edition. The MIT Press, Cambridge, MA, 2002.
    • (2002) The Handbook of Brain Theory and Neural Networks
    • Bottou, L.1    Murata, N.2
  • 16
    • 0000147488 scopus 로고    scopus 로고
    • Online model selection based on the variational Bayes
    • M.A. Sato. Online model selection based on the variational Bayes. Neural Computation, 13(7):1649-1681, 2001.
    • (2001) Neural Computation , vol.13 , Issue.7 , pp. 1649-1681
    • Sato, M.A.1
  • 20
    • 0002788893 scopus 로고    scopus 로고
    • A view of the em algorithm that justifies incremental, sparse, and other variants
    • R.M. Neal and G.E. Hinton. A view of the EM algorithm that justifies incremental, sparse, and other variants. Learning in graphical models, 89:355-368, 1998.
    • (1998) Learning in Graphical Models , vol.89 , pp. 355-368
    • Neal, R.M.1    Hinton, G.E.2
  • 21
    • 0034131785 scopus 로고    scopus 로고
    • On-line em algorithm for the normalized Gaussian network
    • M.A. Sato and S. Ishii. On-line EM algorithm for the normalized Gaussian network. Neural Computation, 12(2):407-432, 2000.
    • (2000) Neural Computation , vol.12 , Issue.2 , pp. 407-432
    • Sato, M.A.1    Ishii, S.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.