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Volumn , Issue , 2008, Pages

Predictive matrix-variate t models

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; LEARNING SYSTEMS;

EID: 85162059520     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (14)

References (13)
  • 2
    • 0002051628 scopus 로고    scopus 로고
    • Empirical analysis of predictive algorithms for collaborative filtering
    • J. Breese, D. Heckerman, and C. Kadie. Empirical analysis of predictive algorithms for collaborative filtering. In UAI-98, pages 43-52, 1998.
    • (1998) UAI-98 , pp. 43-52
    • Breese, J.1    Heckerman, D.2    Kadie, C.3
  • 3
    • 0142215333 scopus 로고    scopus 로고
    • Log-det heuristic for matrix rank minimization with applications to hankel and euclidean distance matrices
    • M. Fazel, H. Haitham, and S. P. Boyd. Log-det heuristic for matrix rank minimization with applications to hankel and euclidean distance matrices. In Proceedings of the American Control Conference, 2003.
    • (2003) Proceedings of the American Control Conference
    • Fazel, M.1    Haitham, H.2    Boyd, S.P.3
  • 4
    • 33746447668 scopus 로고    scopus 로고
    • Multivariate Student-t regression models: Pitfalls and inference
    • C. Fernandez and M. F. J. Steel. Multivariate Student-t regression models: Pitfalls and inference. Biometrika, 86(1):153-167, 1999. (Pubitemid 129767138)
    • (1999) Biometrika , vol.86 , Issue.1 , pp. 153-167
    • Fernandez, C.1    Steel, M.F.J.2
  • 7
    • 27844605876 scopus 로고    scopus 로고
    • Probabilistic non-linear principal component analysis with gaussian process latent variable models
    • N. Lawrence. Probabilistic non-linear principal component analysis with gaussian process latent variable models. J. Mach. Learn. Res., 6:1783-1816, 2005.
    • (2005) J. Mach. Learn. Res. , vol.6 , pp. 1783-1816
    • Lawrence, N.1
  • 8
    • 0000597408 scopus 로고    scopus 로고
    • Comparison of approximate methods for handling hyperparameters
    • D. J. C. MacKay. Comparison of approximate methods for handling hyperparameters. Neural Comput., 11(5):1035-1068, 1999.
    • (1999) Neural Comput. , vol.11 , Issue.5 , pp. 1035-1068
    • MacKay, D.J.C.1
  • 9
    • 31844451557 scopus 로고    scopus 로고
    • Fast maximum margin matrix factorization for collaborative prediction
    • J. D. M. Rennie and N. Srebro. Fast maximum margin matrix factorization for collaborative prediction. In ICML, 2005.
    • (2005) ICML
    • Rennie, J.D.M.1    Srebro, N.2
  • 10
    • 0001224048 scopus 로고    scopus 로고
    • Sparse bayesian learning and the relevance vector machine
    • DOI 10.1162/15324430152748236
    • M. E. Tipping. Sparse bayesian learning and the relevance vector machine. Journal of Machine Learning Research, 1:211-244, 2001. (Pubitemid 33687203)
    • (2001) Journal of Machine Learning Research , vol.1 , Issue.3 , pp. 211-244
    • Tipping, M.E.1
  • 13
    • 31844442664 scopus 로고    scopus 로고
    • Learning Gaussian processes from multiple tasks
    • K. Yu, V. Tresp, and A. Schwaighofer. Learning Gaussian processes from multiple tasks. In ICML, 2005.
    • (2005) ICML
    • Yu, K.1    Tresp, V.2    Schwaighofer, A.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.