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Volumn , Issue , 2010, Pages 425-430

Learning Spatial-Temporal Varying Graphs with Applications to Climate Data Analysis

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; GRAPHIC METHODS; LEARNING ALGORITHMS; LEARNING SYSTEMS;

EID: 84866628536     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (8)

References (15)
  • 2
    • 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. Bio-statistics 9:432-441.
    • (2008) Bio-statistics , vol.9 , pp. 432-441
    • Friedman, J.1    Hastie, T.2    Tibshirani, R.3
  • 3
    • 0842288337 scopus 로고    scopus 로고
    • Inferring cellular networks using probabilistic graphical models
    • Friedman, N. 2004. Inferring cellular networks using probabilistic graphical models. Science 303:799-805.
    • (2004) Science , vol.303 , pp. 799-805
    • Friedman, N.1
  • 4
    • 84958171132 scopus 로고    scopus 로고
    • Bayes net graphs to understand co-authorship networks
    • Goldenberg, A., and Moore, A. W. 2005. Bayes net graphs to understand co-authorship networks. In LinkKDD.
    • (2005) LinkKDD
    • Goldenberg, A.1    Moore, A. W.2
  • 8
    • 70350676932 scopus 로고    scopus 로고
    • Learning dynamic temporal graphs for oil-production equipment monitoring system
    • Liu, Y.; Kalagnanam, J. R.; and Johnsen, O. 2009. Learning dynamic temporal graphs for oil-production equipment monitoring system. In ACM SIGKDD.
    • (2009) ACM SIGKDD
    • Liu, Y.1    Kalagnanam, J. R.2    Johnsen, O.3
  • 9
    • 70450277253 scopus 로고    scopus 로고
    • The nonparanormal: Semiparametric estimation of high dimensional undirected graphs
    • Liu, H.; Lafferty, J.; and Wasserman, L. 2009. The nonparanormal: Semiparametric estimation of high dimensional undirected graphs. J. Mach. Learn. Res. 10:2295-2328.
    • (2009) J. Mach. Learn. Res , vol.10 , pp. 2295-2328
    • Liu, H.1    Lafferty, J.2    Wasserman, L.3
  • 11
    • 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. The Annals of Stat. 34:1436-1462.
    • (2006) The Annals of Stat , vol.34 , pp. 1436-1462
    • Meinshausen, N.1    Bühlmann, P.2
  • 14
    • 77956512686 scopus 로고    scopus 로고
    • Modeling changing dependency structure in multivariate time series
    • Xuan, X., and Murphy, K. 2007. Modeling changing dependency structure in multivariate time series. In Intl. Conf. on Machine Learning (ICML).
    • (2007) Intl. Conf. on Machine Learning (ICML)
    • Xuan, X.1    Murphy, K.2
  • 15
    • 33947115409 scopus 로고    scopus 로고
    • Model selection and estimation in the gaussian graphical model
    • Yuan, M., and Lin, Y. 2007. Model selection and estimation in the gaussian graphical model. Biometrika 94:19-35.
    • (2007) Biometrika , vol.94 , pp. 19-35
    • Yuan, M.1    Lin, Y.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.