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Volumn 1, Issue 3, 2001, Pages 211-244

Sparse Bayesian Learning and the Relevance Vector Machine

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EID: 0001224048     PISSN: 15324435     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article
Times cited : (6112)

References (38)
  • 6
    • 84898957872 scopus 로고    scopus 로고
    • Improving the accuracy and speed of support vector machines
    • M. C. Mozer, M. I. Jordan, and T. Petsche, editors, MIT Press
    • C. J. C. Burges and B. Schölkopf. Improving the accuracy and speed of support vector machines. In M. C. Mozer, M. I. Jordan, and T. Petsche, editors, Advances in Neural Information Processing Systems 9, pages 375-381. MIT Press, 1997.
    • (1997) Advances in Neural Information Processing Systems , vol.9 , pp. 375-381
    • Burges, C.J.C.1    Schölkopf, B.2
  • 9
    • 0002432565 scopus 로고
    • Multivariate adaptive regression splines
    • J. H. Friedman. Multivariate adaptive regression splines. Annals of Statistics, 19(1):1-141, 1991.
    • (1991) Annals of Statistics , vol.19 , Issue.1 , pp. 1-141
    • Friedman, J.H.1
  • 12
    • 0034271876 scopus 로고    scopus 로고
    • The evidence framework applied to support vector machines
    • J. T.-K. K wok. The evidence framework applied to support vector machines. IEEE Transactions on Neural Networks, 11(5):1162-1173, 2000.
    • (2000) IEEE Transactions on Neural Networks , vol.11 , Issue.5 , pp. 1162-1173
    • Wok, J.T.-K.K.1
  • 13
    • 0001025418 scopus 로고
    • Bayesian interpolation
    • D. J. C. MacKay. Bayesian interpolation. Neural Computation, 4(3):415-447, 1992a.
    • (1992) Neural Computation , vol.4 , Issue.3 , pp. 415-447
    • MacKay, D.J.C.1
  • 14
    • 0000234257 scopus 로고
    • The evidence framework applied to classification networks
    • D. J. C. MacKay. The evidence framework applied to classification networks. Neural Computation, 4(5):720-736, 1992b.
    • (1992) Neural Computation , vol.4 , Issue.5 , pp. 720-736
    • MacKay, D.J.C.1
  • 15
    • 0000335983 scopus 로고
    • Bayesian methods for backpropagation networks
    • E. Domany, J. L. van Hemmen, and K. Schulten, editors, chapter 6, Springer
    • D. J. C. MacKay. Bayesian methods for backpropagation networks. In E. Domany, J. L. van Hemmen, and K. Schulten, editors, Models of Neural Networks III, chapter 6, pages 211-254. Springer, 1994.
    • (1994) Models of Neural Networks III , pp. 211-254
    • MacKay, D.J.C.1
  • 16
    • 0003319647 scopus 로고    scopus 로고
    • Introduction to Gaussian processes
    • C. M. Bishop, editor, Springer
    • D. J. C. MacKay. Introduction to Gaussian processes. In C. M. Bishop, editor, Neural Networks and Machine Learning, pages 133-165. Springer, 1998.
    • (1998) Neural Networks and Machine Learning , pp. 133-165
    • MacKay, D.J.C.1
  • 17
    • 0000597408 scopus 로고    scopus 로고
    • Comparison of approximate methods for handling hyperparameters
    • D. J. C. MacKay. Comparison of approximate methods for handling hyperparameters. Neural Computation, 11(5):1035-1068, 1999.
    • (1999) Neural Computation , vol.11 , Issue.5 , pp. 1035-1068
    • MacKay, D.J.C.1
  • 21
    • 0003243224 scopus 로고    scopus 로고
    • Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods
    • A. J. Smola, P. Bartlett, B. Schölkopf, and D. Schuurmans, editors, MIT Press
    • J. Platt. Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. In A. J. Smola, P. Bartlett, B. Schölkopf, and D. Schuurmans, editors, Advances in Large Margin Classifiers. MIT Press, 2000.
    • (2000) Advances in Large Margin Classifiers
    • Platt, J.1
  • 28
    • 84898947199 scopus 로고    scopus 로고
    • Bayesian model selection for support vector machines, Gaussian processes and other kernel classifiers
    • S. A. Solla, T. K. Leen, and K.-R. Müller, editors, MIT Press
    • M. Seeger. Bayesian model selection for support vector machines, Gaussian processes and other kernel classifiers. In S. A. Solla, T. K. Leen, and K.-R. Müller, editors, Advances in Neural Information Processing Systems 12, pages 603-609. MIT Press, 2000.
    • (2000) Advances in Neural Information Processing Systems , vol.12 , pp. 603-609
    • Seeger, M.1
  • 29
    • 0032098361 scopus 로고    scopus 로고
    • The connection between regularization operators and support vector kernels
    • A. J. Smola, B. Schölkopf, and K.-R. Müller. The connection between regularization operators and support vector kernels. Neural Networks, 11:637-649, 1998.
    • (1998) Neural Networks , vol.11 , pp. 637-649
    • Smola, A.J.1    Schölkopf, B.2    Müller, K.-R.3
  • 31
    • 84899032333 scopus 로고    scopus 로고
    • Probabilistic methods for support vector machines
    • S. A. Solla, T. K. Leen, and K.-R. Müller, editors, MIT Press
    • P. Sollich. Probabilistic methods for support vector machines. In S. A. Solla, T. K. Leen, and K.-R. Müller, editors, Advances in Neural Information Processing Systems 12, pages 349-355. MIT Press, 2000.
    • (2000) Advances in Neural Information Processing Systems , vol.12 , pp. 349-355
    • Sollich, P.1
  • 32
    • 84899032239 scopus 로고    scopus 로고
    • The Relevance Vector Machine
    • S. A. Solla, T. K. Leen, and K.-R. Müller, editors, MIT Press
    • M. E. Tipping. The Relevance Vector Machine. In S. A. Solla, T. K. Leen, and K.-R. Müller, editors, Advances in Neural Information Processing Systems 12, pages 652-658. MIT Press, 2000.
    • (2000) Advances in Neural Information Processing Systems , vol.12 , pp. 652-658
    • Tipping, M.E.1
  • 35
    • 84887252594 scopus 로고    scopus 로고
    • Support vector method for function approximation, regression estimation and signal processing
    • M. C. Mozer, M. I. Jordan, and T. Petsche, editors, MIT Press
    • V. N. Vapnik, S. E. Golowich, and A. J. Smola. Support vector method for function approximation, regression estimation and signal processing. In M. C. Mozer, M. I. Jordan, and T. Petsche, editors, Advances in Neural Information Processing Systems 9. MIT Press, 1997.
    • (1997) Advances in Neural Information Processing Systems , vol.9
    • Vapnik, V.N.1    Golowich, S.E.2    Smola, A.J.3
  • 36
    • 0003017575 scopus 로고    scopus 로고
    • Prediction with Gaussian processes: From linear regression to linear prediction and beyond
    • M. I. Jordan, editor, MIT Press
    • C. K. I. Williams. Prediction with Gaussian processes: from linear regression to linear prediction and beyond. In M. I. Jordan, editor, Learning in Graphical Models, pages 599-621. MIT Press, 1999.
    • (1999) Learning in Graphical Models , pp. 599-621
    • Williams, C.K.I.1
  • 38
    • 0000673452 scopus 로고
    • Bayesian regularisation and pruning using a Laplace prior
    • P. M. Williams. Bayesian regularisation and pruning using a Laplace prior. Neural Computation, 7(1):117-143, 1995.
    • (1995) Neural Computation , vol.7 , Issue.1 , pp. 117-143
    • Williams, P.M.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.