메뉴 건너뛰기




Volumn 21, Issue 1, 2008, Pages 36-47

Sequential Bayesian kernel modelling with non-Gaussian noise

Author keywords

[No Author keywords available]

Indexed keywords

APPROXIMATION THEORY; COVARIANCE MATRIX; GAUSSIAN DISTRIBUTION; GAUSSIAN NOISE (ELECTRONIC); TIME SERIES ANALYSIS; VECTORS;

EID: 39649120551     PISSN: 08936080     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.neunet.2007.08.001     Document Type: Article
Times cited : (5)

References (34)
  • 1
    • 84898964031 scopus 로고    scopus 로고
    • A variational Bayesian framework for graphical models
    • Leen T., et al. (Ed), MIT Press, Cambridge, MA
    • Attias H. A variational Bayesian framework for graphical models. In: Leen T., et al. (Ed). Advances in neural inf. proc. systems NIPS-12 (2000), MIT Press, Cambridge, MA 209-215
    • (2000) Advances in neural inf. proc. systems NIPS-12 , pp. 209-215
    • Attias, H.1
  • 2
    • 39649119743 scopus 로고    scopus 로고
    • Beal, M. 2003. Variational algorithms for approximate Bayesian inference. Ph.D. Thesis. London, UK: Gatsby Computational Neuroscience Unit, University College
    • Beal, M. 2003. Variational algorithms for approximate Bayesian inference. Ph.D. Thesis. London, UK: Gatsby Computational Neuroscience Unit, University College
  • 3
    • 0007725224 scopus 로고    scopus 로고
    • Variational relevance vector machines
    • Boutilier C., and Goldszmidt M. (Eds), Morgan Kaufmann, San Mateo, CA
    • Bishop C.M., and Tipping M.E. Variational relevance vector machines. In: Boutilier C., and Goldszmidt M. (Eds). Proc. 16th conf. on uncertainty in artificial intelligence (2000), Morgan Kaufmann, San Mateo, CA 46-53
    • (2000) Proc. 16th conf. on uncertainty in artificial intelligence , pp. 46-53
    • Bishop, C.M.1    Tipping, M.E.2
  • 5
    • 3543070663 scopus 로고    scopus 로고
    • Fisher scoring and a mixture of modes approach for approximate inference and learning in nonlinear state space models
    • Kearns M.S., Solla S.A., and Cohn D.A. (Eds), MIT Press, Cambridge, MA
    • Briegel T., and Tresp V. Fisher scoring and a mixture of modes approach for approximate inference and learning in nonlinear state space models. In: Kearns M.S., Solla S.A., and Cohn D.A. (Eds). Advances in neural inf. proc. systems NIPS-11 (1999), MIT Press, Cambridge, MA 403-409
    • (1999) Advances in neural inf. proc. systems NIPS-11 , pp. 403-409
    • Briegel, T.1    Tresp, V.2
  • 6
    • 27744490126 scopus 로고    scopus 로고
    • Constructing Bayesian formulations of sparse kernel learning methods
    • Cawley G.C., and Talbot N.L.C. Constructing Bayesian formulations of sparse kernel learning methods. Neural Networks 18 5-6 (2005) 674-683
    • (2005) Neural Networks , vol.18 , Issue.5-6 , pp. 674-683
    • Cawley, G.C.1    Talbot, N.L.C.2
  • 8
    • 0039770574 scopus 로고    scopus 로고
    • Hierarchical Bayesian-Kalman models for regularisation and ARD in sequential learning
    • de Freitas J.F.G., Niranjan M., and Gee A.H. Hierarchical Bayesian-Kalman models for regularisation and ARD in sequential learning. Neural Computation 12 4 (2000) 933-953
    • (2000) Neural Computation , vol.12 , Issue.4 , pp. 933-953
    • de Freitas, J.F.G.1    Niranjan, M.2    Gee, A.H.3
  • 10
    • 84958972434 scopus 로고    scopus 로고
    • A variational approach to robust regression
    • Dorffner G., Bischof H., and Hornik K. (Eds), Springer, Berlin
    • Faul A., and Tipping M.E. A variational approach to robust regression. In: Dorffner G., Bischof H., and Hornik K. (Eds). Proc. int. conf. artificial neural networks (2001), Springer, Berlin 95-102
    • (2001) Proc. int. conf. artificial neural networks , pp. 95-102
    • Faul, A.1    Tipping, M.E.2
  • 11
    • 84899003086 scopus 로고    scopus 로고
    • Propagation algorithms for variational Bayesian learning
    • Leen T.K., Dietterich T., and Tresp V. (Eds), MIT Press, Cambridge, MA
    • Ghahramani Z., and Beal M. Propagation algorithms for variational Bayesian learning. In: Leen T.K., Dietterich T., and Tresp V. (Eds). Advances in neural inf. proc. systems NIPS-13 (2001), MIT Press, Cambridge, MA 507-513
    • (2001) Advances in neural inf. proc. systems NIPS-13 , pp. 507-513
    • Ghahramani, Z.1    Beal, M.2
  • 13
    • 0038563987 scopus 로고
    • Diagnostics for use with regression recursive residuals
    • Hawkins D.M. Diagnostics for use with regression recursive residuals. Technometrics 33 (1991) 221-234
    • (1991) Technometrics , vol.33 , pp. 221-234
    • Hawkins, D.M.1
  • 14
    • 0012347694 scopus 로고    scopus 로고
    • Haykin S. (Ed), John Wiley and Sons, New York, NY
    • In: Haykin S. (Ed). Kalman filtering and neural networks (2001), John Wiley and Sons, New York, NY
    • (2001) Kalman filtering and neural networks
  • 15
    • 0004262735 scopus 로고
    • John Wiley and Sons, New York, NY
    • Huber P.J. Robust statistics (1981), John Wiley and Sons, New York, NY
    • (1981) Robust statistics
    • Huber, P.J.1
  • 16
    • 0033225865 scopus 로고    scopus 로고
    • An introduction to variational methods for graphical models
    • Jordan M.I., Ghahramani Z., Jaakkola T., and Saul L.K. An introduction to variational methods for graphical models. Machine Learning 37 2 (1999) 183-233
    • (1999) Machine Learning , vol.37 , Issue.2 , pp. 183-233
    • Jordan, M.I.1    Ghahramani, Z.2    Jaakkola, T.3    Saul, L.K.4
  • 18
    • 0001025418 scopus 로고
    • Bayesian interpolation
    • MacKay D.J.C. Bayesian interpolation. Neural Computation 4 3 (1992) 415-447
    • (1992) Neural Computation , vol.4 , Issue.3 , pp. 415-447
    • MacKay, D.J.C.1
  • 19
    • 0038765941 scopus 로고
    • Developments in probabilistic modelling with neural networks- ensemble learning
    • Kappen B., and Gielen S. (Eds), Springer-Verlag, Berlin
    • MacKay D.J.C. Developments in probabilistic modelling with neural networks- ensemble learning. In: Kappen B., and Gielen S. (Eds). Proc. 3rd annual symposium on neural networks (1995), Springer-Verlag, Berlin 191-198
    • (1995) Proc. 3rd annual symposium on neural networks , pp. 191-198
    • MacKay, D.J.C.1
  • 21
    • 0027205884 scopus 로고
    • A Scaled Conjugate Gradient algorithm for fast supervised learning
    • Möller A.F. A Scaled Conjugate Gradient algorithm for fast supervised learning. Neural Networks 6 4 (1993) 525-533
    • (1993) Neural Networks , vol.6 , Issue.4 , pp. 525-533
    • Möller, A.F.1
  • 22
    • 39649106993 scopus 로고    scopus 로고
    • Neal, R. 2006. Software for flexible Bayesian modelling.http://www.cs.toronto.edu/∼radford/fbm.software.html Toronto, CA
    • Neal, R. 2006. Software for flexible Bayesian modelling.http://www.cs.toronto.edu/∼radford/fbm.software.html Toronto, CA
  • 24
    • 0034504542 scopus 로고    scopus 로고
    • Variational Bayes for non-Gaussian autoregressive models
    • Widrow B., et al. (Ed), IEEE Press, New York
    • Penny W.D., and Roberts S.J. Variational Bayes for non-Gaussian autoregressive models. In: Widrow B., et al. (Ed). Proc. IEEE workshop on neural networks for signal processing (2000), IEEE Press, New York 135-144
    • (2000) Proc. IEEE workshop on neural networks for signal processing , pp. 135-144
    • Penny, W.D.1    Roberts, S.J.2
  • 26
    • 0036729732 scopus 로고    scopus 로고
    • Variational bayes for generalised autoregressive models
    • Roberts S.J., and Penny W.D. Variational bayes for generalised autoregressive models. IEEE Transactions on Signal Processing 50 9 (2002) 2245-2257
    • (2002) IEEE Transactions on Signal Processing , vol.50 , Issue.9 , pp. 2245-2257
    • Roberts, S.J.1    Penny, W.D.2
  • 27
    • 0038582146 scopus 로고    scopus 로고
    • Incremental Sparse Kernel Machine
    • Proc. int. conf. artificial neural networks. Dorronsoro J.R. (Ed), Springer-Verlag, Berlin
    • Sato M., and Oba S. Incremental Sparse Kernel Machine. In: Dorronsoro J.R. (Ed). Proc. int. conf. artificial neural networks. LNCS Vol. 2415 (2002), Springer-Verlag, Berlin 700-706
    • (2002) LNCS , vol.2415 , pp. 700-706
    • Sato, M.1    Oba, S.2
  • 30
    • 0000779360 scopus 로고
    • Detecting Strange Attractors in Turbulence
    • Dynamical systems and turbulence. Rand D.A., and Young L.-S. (Eds), Springer-Verlag, Berlin
    • Takens F. Detecting Strange Attractors in Turbulence. In: Rand D.A., and Young L.-S. (Eds). Dynamical systems and turbulence. Lecture notes in mathematics Vol. 898 (1981), Springer-Verlag, Berlin 366-381
    • (1981) Lecture notes in mathematics , vol.898 , pp. 366-381
    • Takens, F.1
  • 31
    • 0001224048 scopus 로고    scopus 로고
    • Sparse Bayesian learning and the relevance vector machine
    • Tipping M.E. Sparse Bayesian learning and the relevance vector machine. Journal of Machine Learning Research 1 (2001) 211-244
    • (2001) Journal of Machine Learning Research , vol.1 , pp. 211-244
    • Tipping, M.E.1
  • 32
    • 27844592624 scopus 로고    scopus 로고
    • Variational inference for Student-t models: Robust Bayesian interpolation and generalised component analysis
    • Tipping M.E., and Lawrence N.D. Variational inference for Student-t models: Robust Bayesian interpolation and generalised component analysis. Neurocomputing 69 (2005) 123-141
    • (2005) Neurocomputing , vol.69 , pp. 123-141
    • Tipping, M.E.1    Lawrence, N.D.2
  • 34
    • 0003359612 scopus 로고    scopus 로고
    • The unscented Kalman filter
    • Haykin S. (Ed), Wiley Publ, New York
    • Wan E.A., and van der Merwe R. The unscented Kalman filter. In: Haykin S. (Ed). Kalman filtering and neural networks (2001), Wiley Publ, New York 221-280
    • (2001) Kalman filtering and neural networks , pp. 221-280
    • Wan, E.A.1    van der Merwe, R.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.