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Volumn 2, Issue , 2015, Pages 1481-1490

Distributed Gaussian processes

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; GAUSSIAN DISTRIBUTION; GAUSSIAN NOISE (ELECTRONIC);

EID: 84969832855     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (330)

References (28)
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    • Selecting weighting factors in logarithmic opinion pools
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    • Heskes, Tom. Selecting Weighting Factors in Logarithmic Opinion Pools. In Advances in Neural Information Processing Systems, pp. 266-272. Morgan Kaufman, 1998.
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    • Heskes, T.1
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    • Adaptive mixtures of local experts
    • Jacobs, Robert A., Jordan, Michael I., Nowlan, Steven J., and Hinton, Geoffrey E. Adaptive Mixtures of Local Experts. Neural Computation, 3:79-87, 1991. URL http://www.cs.toronto.edu/~hinton/absps/jjnh91.pdf.
    • (1991) Neural Computation , vol.3 , pp. 79-87
    • Jacobs, R.A.1    Jordan, M.I.2    Nowlan, S.J.3    Hinton, G.E.4
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    • Efficient global optimization of expensive black-box functions
    • December
    • Jones, Donald R., Schonlau, Matthias, and Welch, William J. Efficient Global Optimization of Expensive Black-Box Functions. Journal of Global Optimization, 13(4):455-492, December 1998. doi: 10.1023/A: 1008306431147.
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    • Jones, D.R.1    Schonlau, M.2    Welch, W.J.3
  • 12
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    • Near-optimal sensor placements in Gaussian processes: Theory, efficient algorithms and empirical studies
    • February
    • Krause, Andreas, Singh, Ajit, and Guestrin, Carlos. Near-Optimal Sensor Placements in Gaussian Processes: Theory, Efficient Algorithms and Empirical Studies. Journal of Machine Learning Research, 9:235-284, February 2008. URL http://www.jmlr.org/papers/volume9/krause08a/krause08a.pdf.
    • (2008) Journal of Machine Learning Research , vol.9 , pp. 235-284
    • Krause, A.1    Singh, A.2    Guestrin, C.3
  • 13
    • 27844605876 scopus 로고    scopus 로고
    • Probabilistic non-linear principal component analysis with Gaussian process latent variable models
    • November
    • Lawrence, Neil. Probabilistic Non-linear Principal Component Analysis with Gaussian Process Latent Variable Models. Journal of Machine Learning Research, 6:1783-1816, November 2005. URL http://www.jmlr.org/papers/volume6/Iawrence05a/lawrence05a.pdf.
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    • Lawrence, N.1
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    • A unifying view of sparse approximate Gaussian process regression
    • Quiñonero-Candela, Joaquin and Rasmussen, Carl E. A Unifying View of Sparse Approximate Gaussian Process Regression. Journal of Machine Learning Research, 6(2): 1939-1960, 2005. URL http://jmlr.csail.mit.edu/papers/volume6/quinonero-candela05a/quinonero-candela05a.pdf.
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    • Fast forward selection to speed up sparse Gaussian process regression
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    • Seeger, M.1    Williams, C.K.I.2    Lawrence, N.D.3
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    • A Bayesian committee machine
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* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.