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Volumn 5, Issue , 2009, Pages 567-574

Variational learning of inducing variables in sparse Gaussian processes

Author keywords

[No Author keywords available]

Indexed keywords

GAUSSIAN PROCESSES; HYPERPARAMETERS; KULLBACK LEIBLER DIVERGENCE; LATENT FUNCTION; LOWER BOUNDS; MARGINAL LIKELIHOOD; POSTERIOR DISTRIBUTIONS; SPARSE APPROXIMATIONS; VARIATIONAL FORMULATION; VARIATIONAL PARAMETERS;

EID: 84860609370     PISSN: 15324435     EISSN: 15337928     Source Type: Journal    
DOI: None     Document Type: Conference Paper
Times cited : (1388)

References (12)
  • 1
    • 0038891993 scopus 로고    scopus 로고
    • Sparse online Gaussian processes
    • Csato, L. and Opper, M. (2002). Sparse online Gaussian processes. Neural Computation, 14:641-668.
    • (2002) Neural Computation , vol.14 , pp. 641-668
    • Csato, L.1    Opper, M.2
  • 5
    • 84898940342 scopus 로고    scopus 로고
    • Transductive and inductive methods for approximate Gaussian process regression
    • MIT Press
    • Schwaighofer, A. and Tresp, V. (2003). Transductive and inductive methods for approximate Gaussian process regression. In Neural Information Processing Systems 15. MIT Press.
    • (2003) Neural Information Processing Systems , vol.15
    • Schwaighofer, A.1    Tresp, V.2
  • 11
    • 84862292378 scopus 로고    scopus 로고
    • Variational model selection for sparse gaussian process regression
    • School of Computer Science
    • Titsias, M. K. (2009). Variational Model Selection for Sparse Gaussian Process Regression. Technical report, School of Computer Science, University of Manchester.
    • (2009) Technical Report, University of Manchester
    • Titsias, M.K.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.