메뉴 건너뛰기




Volumn 207, Issue , 2006, Pages 403-418

Bayesian support vector machines for feature ranking and selection

Author keywords

[No Author keywords available]

Indexed keywords


EID: 34047174522     PISSN: 14349922     EISSN: None     Source Type: Book Series    
DOI: 10.1007/978-3-540-35488-8_19     Document Type: Article
Times cited : (3)

References (33)
  • 1
    • 85099479344 scopus 로고
    • Learning with many irrelevant features
    • MIT Press
    • H. Almuallim and T. G. Dietterich. Learning with many irrelevant features. In Proc. AAAI-91, pages 547-552. MIT Press, 1991.
    • (1991) Proc. AAAI-91 , pp. 547-552
    • Almuallim, H.1    Dietterich, T.G.2
  • 3
    • 27144489164 scopus 로고    scopus 로고
    • A tutorial on support vector machines for pattern recogintion
    • C. J. G. Burges. A tutorial on support vector machines for pattern recogintion. Data Mining and Knowledge Discovery, 2 (2):121-167, 1998.
    • (1998) Data Mining and Knowledge Discovery , vol.2 , Issue.2 , pp. 121-167
    • Burges, C.J.G.1
  • 4
    • 0042326376 scopus 로고    scopus 로고
    • Bayesian trigonometric support vector classifier
    • W. Chu, S. S. Keerthi, and C. J. Ong. Bayesian trigonometric support vector classifier. Neural Computation, 15(9):2227-2254, 2003.
    • (2003) Neural Computation , vol.15 , Issue.9 , pp. 2227-2254
    • Chu, W.1    Keerthi, S.S.2    Ong, C.J.3
  • 5
    • 1242331293 scopus 로고    scopus 로고
    • Bayesian support vector regression using a unified loss function
    • W. Chu, S. S. Keerthi, and C. J. Ong. Bayesian support vector regression using a unified loss function. IEEE transactions on neural networks, 15(l):29-44, 2004.
    • (2004) IEEE transactions on neural networks , vol.15 , Issue.L , pp. 29-44
    • Chu, W.1    Keerthi, S.S.2    Ong, C.J.3
  • 7
    • 34047160784 scopus 로고    scopus 로고
    • T. Evgeniou, M. Pontil, and T. Poggio. A unified framework for regularization networks and support vector machines. A.I. Memo 1654, MIT, 1999.
    • T. Evgeniou, M. Pontil, and T. Poggio. A unified framework for regularization networks and support vector machines. A.I. Memo 1654, MIT, 1999.
  • 10
    • 0036161259 scopus 로고    scopus 로고
    • Gene selection for cancer classification using support vector machines
    • I. Guyon, J. Weston, and S. Barnhill. Gene selection for cancer classification using support vector machines. Machine Learning, 40:389-422, 2002.
    • (2002) Machine Learning , vol.40 , pp. 389-422
    • Guyon, I.1    Weston, J.2    Barnhill, S.3
  • 12
    • 85099325734 scopus 로고
    • Irrelevant features and the subset selection problem
    • Morgan Kaufmann Publishers
    • G. John, R. Kohavi, and K. Pfleger. Irrelevant features and the subset selection problem. In Proc. ML-94, pages 121-129. Morgan Kaufmann Publishers, 1994.
    • (1994) Proc. ML-94 , pp. 121-129
    • John, G.1    Kohavi, R.2    Pfleger, K.3
  • 13
    • 0000545946 scopus 로고    scopus 로고
    • Improvements to Platt's SMO algorithm for SVM classifier design
    • March
    • S. S. Keerthi, S. K. Shevade, C. Bhattacharyya, and K. R. K. Murthy. Improvements to Platt's SMO algorithm for SVM classifier design. Neural Computation, 13 (3):637-649, March 2001.
    • (2001) Neural Computation , vol.13 , Issue.3 , pp. 637-649
    • Keerthi, S.S.1    Shevade, S.K.2    Bhattacharyya, C.3    Murthy, K.R.K.4
  • 16
    • 0027002164 scopus 로고
    • The feature selection problem: Traditional methods and a new algorithm
    • MIT Press
    • K. Kira and L. A. Rendell. The feature selection problem: Traditional methods and a new algorithm. In Proc. AAAI-92, pages 129-134. MIT Press, 1992.
    • (1992) Proc. AAAI-92 , pp. 129-134
    • Kira, K.1    Rendell, L.A.2
  • 17
    • 0001901666 scopus 로고
    • Induction of selective Bayesian classifiers
    • Morgan Kaufmann
    • P. Langley and S. Sage. Induction of selective Bayesian classifiers. In Proc. UAI-94, pages 399-406. Morgan Kaufmann, 1994.
    • (1994) Proc. UAI-94 , pp. 399-406
    • Langley, P.1    Sage, S.2
  • 18
    • 0002704818 scopus 로고
    • A practical Bayesian framework for back propagation networks
    • D. J. C. MacKay. A practical Bayesian framework for back propagation networks. Neural Computation, 4(3):448-472, 1992.
    • (1992) Neural Computation , vol.4 , Issue.3 , pp. 448-472
    • MacKay, D.J.C.1
  • 19
    • 0000335983 scopus 로고
    • Bayesian methods for backpropagation networks
    • D. J. C. MacKay. Bayesian methods for backpropagation networks. Models of Neural Networks III, pages 211-254, 1994.
    • (1994) Models of Neural Networks III , pp. 211-254
    • MacKay, D.J.C.1
  • 21
    • 0003611509 scopus 로고    scopus 로고
    • Bayesian Learning for Neural Networks
    • Springer
    • R. M. Neal. Bayesian Learning for Neural Networks. Lecture Notes in Statistics. Springer, 1996.
    • (1996) Lecture Notes in Statistics
    • Neal, R.M.1
  • 22
    • 0004220749 scopus 로고    scopus 로고
    • Monte Carlo implementation of Gaussian process models for Bayesian regression and classification
    • Technical Report No. 9702, Department of Statistics, University of Toronto
    • R. M. Neal. Monte Carlo implementation of Gaussian process models for Bayesian regression and classification. Technical Report No. 9702, Department of Statistics, University of Toronto, 1997a.
    • (1997)
    • Neal, R.M.1
  • 23
    • 34047176733 scopus 로고    scopus 로고
    • R. M. Neal. Regression and classification using Gaussian process priors (with discussion). In J. M. Bernerdo, J. O. Berger, A. P. Dawid, and A. P. M. Smith, editors, Bayesian Statistics, 6, 1997b.
    • R. M. Neal. Regression and classification using Gaussian process priors (with discussion). In J. M. Bernerdo, J. O. Berger, A. P. Dawid, and A. P. M. Smith, editors, Bayesian Statistics, volume 6, 1997b.
  • 24
    • 0034320350 scopus 로고    scopus 로고
    • Gaussian processes for classification: Mean field algorithm
    • M. Opper and O. Winther. Gaussian processes for classification: Mean field algorithm. Neural Computation, 12(11):2655-2684, 2000.
    • (2000) Neural Computation , vol.12 , Issue.11 , pp. 2655-2684
    • Opper, M.1    Winther, O.2
  • 25
    • 0003120218 scopus 로고    scopus 로고
    • Fast training of support vector machines using sequential minimal optimization
    • B. Schölkopf, C. J. C. Burges, and A. J. Smola, editors, MIT Press
    • J. C. Platt. Fast training of support vector machines using sequential minimal optimization. In B. Schölkopf, C. J. C. Burges, and A. J. Smola, editors, Advances in Kernel Methods - Support Vector Learning, pages 185-208. MIT Press, 1999.
    • (1999) Advances in Kernel Methods - Support Vector Learning , pp. 185-208
    • Platt, J.C.1
  • 27
    • 0001767260 scopus 로고    scopus 로고
    • Bayesian model selection for support vector machines, Gaussian processes and other kernel classifiers
    • M. Seeger. Bayesian model selection for support vector machines, Gaussian processes and other kernel classifiers. In Advances in Neural Information Processing Systems, volume 12, 1999.
    • (1999) Advances in Neural Information Processing Systems , vol.12
    • Seeger, M.1
  • 28
    • 0003401675 scopus 로고    scopus 로고
    • A tutorial on support vector regression
    • Technical Report NC2-TR-1998-030, GMD First, October
    • A. J. Smola and B. Schölkopf. A tutorial on support vector regression. Technical Report NC2-TR-1998-030, GMD First, October 1998.
    • (1998)
    • Smola, A.J.1    Schölkopf, B.2
  • 33
    • 0002295913 scopus 로고    scopus 로고
    • Gaussian processes for regression
    • D. S. Touretzky, M. C. Mozer, and M. E. Hasselmo, editors, MIT Press
    • C. K. I. Williams and C. E. Rasmussen. Gaussian processes for regression. In D. S. Touretzky, M. C. Mozer, and M. E. Hasselmo, editors, Advances in Neural Information Processing Systems, volume 8, pages 598-604, 1996. MIT Press.
    • (1996) Advances in Neural Information Processing Systems , vol.8 , pp. 598-604
    • Williams, C.K.I.1    Rasmussen, C.E.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.