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Volumn 1, Issue , 2010, Pages 382-387

Latent variable model for learning in Pairwise Markov Networks

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; SENSOR NETWORKS;

EID: 77958611156     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (3)

References (12)
  • 2
    • 41549101939 scopus 로고    scopus 로고
    • Model selection through sparse maximum likelihood estimation for multivariate gaussian or binary data
    • Banerjee, O.; Ghaoui, L. E.; and d'Aspremont, A. 2008. Model selection through sparse maximum likelihood estimation for multivariate gaussian or binary data. Journal of Machine Learning Research 9:485-516.
    • (2008) Journal of Machine Learning Research , vol.9 , pp. 485-516
    • Banerjee, O.1    Ghaoui, L.E.2    D'Aspremont, A.3
  • 3
    • 45849134070 scopus 로고    scopus 로고
    • Sparse inverse covariance estimation with the graphical lasso
    • Friedman, J.; Hastie, T.; and Tibshirani, R. 2008. Sparse inverse covariance estimation with the graphical lasso. J. Biostatis-lies 9(3):432-44l.
    • (2008) J. Biostatis-lies , vol.9 , Issue.3
    • Friedman, J.1    Hastie, T.2    Tibshirani, R.3
  • 4
    • 66549109770 scopus 로고    scopus 로고
    • Estimation of sparse binary pairwise markov networks using pseudo-likelihoods
    • Höfling, H., and Tibshirani, R. 2009. Estimation of sparse binary pairwise markov networks using pseudo-likelihoods. J. Mach. Learn. Res. 10:883-906.
    • (2009) J. Mach. Learn. Res. , vol.10 , pp. 883-906
    • Höfling, H.1    Tibshirani, R.2
  • 5
    • 0003641246 scopus 로고
    • On the effective implementation of the iterative proportional fitting procedure
    • Jirouśek, R., and Pr̂euĉil, S. 1995. On the effective implementation of the iterative proportional fitting procedure. Comput. Stat. Data Anal. 19(2): 177-189.
    • (1995) Comput. Stat. Data Anal. , vol.19 , Issue.2 , pp. 177-189
    • Jirouśek, R.1    Pr̂euĉil, S.2
  • 9
    • 70049104366 scopus 로고    scopus 로고
    • Sparse gaussian graphical models with unknown block structure
    • Danyluk, A. P.; Bottou, L.; and Littman, M. L., eds., ICML, ACM
    • Marlin, B. M., and Murphy, K. P. 2009. Sparse gaussian graphical models with unknown block structure. In Danyluk, A. P.; Bottou, L.; and Littman, M. L., eds., ICML, volume 382 of ACM International Conference Proceeding Series, 89. ACM.
    • (2009) ACM International Conference Proceeding Series , vol.382 , pp. 89
    • Marlin, B.M.1    Murphy, K.P.2
  • 10
    • 33747163541 scopus 로고    scopus 로고
    • High-dimensional graphs and variable selection with the lasso
    • Meinshausen, N., and Bühlmann, P. 2006. High-dimensional graphs and variable selection with the lasso. Annals of Statistics 34(3): 1436-1462.
    • (2006) Annals of Statistics , vol.34 , Issue.3 , pp. 1436-1462
    • Meinshausen, N.1    Bühlmann, P.2
  • 11
    • 38049184884 scopus 로고    scopus 로고
    • Modeling highway traffic volumes
    • ECML, Springer
    • Singliar, T, and Hauskrecht, M. 2007. Modeling highway traffic volumes. In ECML, volume 4701 of Lecture Notes in Computer Science, 732-739. Springer.
    • (2007) Lecture Notes in Computer Science , vol.4701 , pp. 732-739
    • Singliar, T.1    Hauskrecht, M.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.