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Volumn 9, Issue , 2010, Pages 964-971

Multi-task learning using generalized t process

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; ASYMPTOTIC ANALYSIS; BAYESIAN NETWORKS; INVERSE PROBLEMS; LEARNING SYSTEMS;

EID: 84862273137     PISSN: 15324435     EISSN: 15337928     Source Type: Journal    
DOI: None     Document Type: Conference Paper
Times cited : (48)

References (27)
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  • 3
    • 0346238931 scopus 로고    scopus 로고
    • Task clustering and gating for Bayesian multitask learning
    • Bakker, B. and Heskes, T. (2003). Task clustering and gating for Bayesian multitask learning. Journal of Machine Learning Research, 4:83-99.
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    • Bakker, B.1    Heskes, T.2
  • 4
    • 0031187873 scopus 로고    scopus 로고
    • A Bayesian/information theoretic model of learning to learn via multiple task sampling
    • Baxter, J. (1997). A Bayesian/information theoretic model of learning to learn via multiple task sampling. Machine Learning, 28(1):7-39.
    • (1997) Machine Learning , vol.28 , Issue.1 , pp. 7-39
    • Baxter, J.1
  • 6
    • 0031189914 scopus 로고    scopus 로고
    • Multitask learning
    • Caruana, R. (1997). Multitask learning. Machine Learning, 28(1):41-75.
    • (1997) Machine Learning , vol.28 , Issue.1 , pp. 41-75
    • Caruana, R.1
  • 13
    • 0343244538 scopus 로고    scopus 로고
    • General bounds on Bayes errors for regression with Gaussian processes
    • Opper, M. and Vivarelli, F. (1998). General bounds on Bayes errors for regression with Gaussian processes. In Advances in Neural Information Processing Systems 11, pages 302-308.
    • (1998) Advances in Neural Information Processing Systems , vol.11 , pp. 302-308
    • Opper, M.1    Vivarelli, F.2
  • 18
    • 0039489976 scopus 로고    scopus 로고
    • Learning curves for Gaussian process regression: Approximations and bounds
    • Sollich, P. and Halees, A. (2002). Learning curves for Gaussian process regression: Approximations and bounds. Neural Computation, 14(6):1393-1428.
    • (2002) Neural Computation , vol.14 , Issue.6 , pp. 1393-1428
    • Sollich, P.1    Halees, A.2
  • 19
    • 84862602372 scopus 로고    scopus 로고
    • Semiparametric latent factor models
    • Teh, Y. W., Seeger, M., and Jordan, M. I. (2005). Semiparametric latent factor models. In AISTATS.
    • (2005) AISTATS
    • Teh, Y.W.1    Seeger, M.2    Jordan, M.I.3
  • 20
    • 85031124575 scopus 로고    scopus 로고
    • Is learning the n-th thing any easier than learning the first?
    • Denver, CO
    • Thrun, S. (1996). Is learning the n-th thing any easier than learning the first? In Advances in Neural Information Processing Systems 8, pages 640-646, Denver, CO.
    • (1996) Advances in Neural Information Processing Systems , vol.8 , pp. 640-646
    • Thrun, S.1
  • 21
    • 84898939890 scopus 로고    scopus 로고
    • On a connection between kernel PCA and metric multidimensional scaling
    • Williams, C. K. I. (2001). On a connection between kernel PCA and metric multidimensional scaling. In Advances in Neural Information Processing Systems 13, pages 675-681.
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    • Williams, C.K.I.1
  • 22
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    • Upper and lower bounds on the learning curve for Gaussian processes
    • Williams, C. K. I. and Vivarelli, F. (2000). Upper and lower bounds on the learning curve for Gaussian processes. Machine Learning, 40(1):77-102.
    • (2000) Machine Learning , vol.40 , Issue.1 , pp. 77-102
    • Williams, C.K.I.1    Vivarelli, F.2
  • 26
    • 36348951585 scopus 로고    scopus 로고
    • Semiparametric regression using student t processes
    • Zhang, Z., Wu, G., and Chang, E. Y. (2007). Semiparametric regression using student t processes. IEEE Transactions on Neural Networks, 18(6):1572-1588.
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* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.