메뉴 건너뛰기




Volumn , Issue , 2013, Pages 1273-1279

Generalized relational topic models with data augmentation

Author keywords

[No Author keywords available]

Indexed keywords

ASYMMETRIC NETWORKS; BAYESIAN INFERENCE; DATA AUGMENTATION; FAST APPROXIMATION; NETWORK STRUCTURES; PREDICTION PERFORMANCE; REGULARIZATION PARAMETERS; VARIATIONAL APPROXIMATION;

EID: 84896062198     PISSN: 10450823     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (25)

References (27)
  • 12
    • 75849131336 scopus 로고    scopus 로고
    • Modeling homophily and stochastic equivalence in symmetric relational data
    • P.D. Hoff. Modeling homophily and stochastic equivalence in symmetric relational data. In Advances in Neural Information Processing Systems, 2007.
    • (2007) Advances in Neural Information Processing Systems
    • Hoff, P.D.1
  • 17
    • 0037399538 scopus 로고    scopus 로고
    • PAC-Bayesian stochastic model selection
    • D. McAllester. PAC-Bayesian stochastic model selection. Machine Learning, 51:5-21, 2003.
    • (2003) Machine Learning , vol.51 , pp. 5-21
    • McAllester, D.1
  • 23
    • 84950758368 scopus 로고
    • The calculation of posterior distributions by data augmentation
    • M. A. Tanner and W. H. Wong. The calculation of posterior distributions by data augmentation. Journal of the Americal Statistical Association, 82(398):528-540, 1987.
    • (1987) Journal of the Americal Statistical Association , vol.82 , Issue.398 , pp. 528-540
    • Tanner, M.A.1    Wong, W.H.2
  • 27
    • 84867115033 scopus 로고    scopus 로고
    • Max-margin nonparametric latent feature models for link prediction
    • J. Zhu. Max-margin nonparametric latent feature models for link prediction. In International Conference on Machine Learning, 2012.
    • (2012) International Conference on Machine Learning
    • Zhu, J.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.