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Volumn , Issue , 2009, Pages 1129-1136

Relative performance guarantees for approximate inference in latent Dirichlet allocation

Author keywords

[No Author keywords available]

Indexed keywords

STATISTICS;

EID: 80053160955     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (16)

References (24)
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  • 8
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    • The author-recipient-topic model for topic and role discovery in social networks: Experiments with Enron and academic email
    • Amherst
    • A. McCallum, A. Corrada-Emmanuel, and X. Wang. The author-recipient-topic model for topic and role discovery in social networks: Experiments with Enron and academic email. Technical report, University of Massachusetts, Amherst, 2004.
    • (2004) Technical Report, University of Massachusetts
    • McCallum, A.1    Corrada-Emmanuel, A.2    Wang, X.3
  • 11
    • 33745155436 scopus 로고    scopus 로고
    • A Bayesian hierarchical model for learning natural scene categories
    • L. Fei-Fei and P. Perona. A Bayesian hierarchical model for learning natural scene categories. IEEE Computer Vision and Pattern Recognition, pages 524-531, 2005.
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    • Fei-Fei, L.1    Perona, P.2
  • 14
    • 33750327222 scopus 로고    scopus 로고
    • LDA-based document models for ad-hoc retrieval
    • X. Wei and B. Croft. LDA-based document models for ad-hoc retrieval. In SIGIR, 2006.
    • (2006) SIGIR
    • Wei, X.1    Croft, B.2
  • 15
    • 36348944093 scopus 로고    scopus 로고
    • Organizing the OCA: Learning faceted subjects from a library of digital books
    • D. Mimno and A. McCallum. Organizing the OCA: Learning faceted subjects from a library of digital books. In Joint Conference on Digital Libraries, 2007.
    • (2007) Joint Conference on Digital Libraries
    • Mimno, D.1    McCallum, A.2
  • 17
    • 84998536010 scopus 로고    scopus 로고
    • Modeling general and specific aspects of documents with a probabilistic topic model
    • C. Chemudugunta, P. Smyth, and M. Steyvers. Modeling general and specific aspects of documents with a probabilistic topic model. In NIPS 19, 2006.
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    • Chemudugunta, C.1    Smyth, P.2    Steyvers, M.3
  • 19
    • 44649094993 scopus 로고    scopus 로고
    • Probabilistic topic models
    • T. Landauer, D. McNamara, S. Dennis, and W. Kintsch, editors. Laurence Erlbaum
    • T. Griffiths and M. Steyvers. Probabilistic topic models. In T. Landauer, D. McNamara, S. Dennis, and W. Kintsch, editors, Latent Semantic Analysis: A Road to Meaning. Laurence Erlbaum, 2006.
    • (2006) Latent Semantic Analysis: A Road to Meaning
    • Griffiths, T.1    Steyvers, M.2
  • 21
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    • A collapsed variational bayesian inference algorithm for latent dirichlet allocation
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    • Teh, Y.1    Newman, D.2    Welling, M.3
  • 23
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    • Introduction to variational methods for graphical models
    • DOI 10.1023/A:1007665907178
    • M. Jordan, Z. Ghahramani, T. Jaakkola, and L. Saul. Introduction to variational methods for graphical models. Machine Learning, 37:183-233, 1999. (Pubitemid 30544678)
    • (1999) Machine Learning , vol.37 , Issue.2 , pp. 183-233
    • Jordan, M.I.1    Ghahramani, Z.2    Jaakkola, T.S.3    Saul, L.K.4
  • 24
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    • Stochastic complexities of Gaussian mixtures in variational Bayesian approximation
    • K. Watanabe and S. Watanabe. Stochastic complexities of gaussian mixtures in variational bayesian approximation. Journal of Machine Learning Research, 7:625-644, 2006. (Pubitemid 43668112)
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* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.