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Volumn , Issue , 2005, Pages 241-248

Hierarchic bayesian models for kernel learning

Author keywords

[No Author keywords available]

Indexed keywords

DATA REDUCTION; HIERARCHICAL SYSTEMS; REGRESSION ANALYSIS;

EID: 31844435594     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1102351.1102382     Document Type: Conference Paper
Times cited : (72)

References (23)
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    • L. K. Saul, Y. Weiss and L. Bottou (Eds.), Cambridge, MA: MIT Press
    • Bach, F. R., Thibaux, R., & Jordan, M. I. (2005). Computing regularization paths for learning multiple kernels. In L. K. Saul, Y. Weiss and L. Bottou (Eds.), Advances in neural information processing systems 17. Cambridge, MA: MIT Press.
    • (2005) Advances in Neural Information Processing Systems , vol.17
    • Bach, F.R.1    Thibaux, R.2    Jordan, M.I.3
  • 6
    • 84899006163 scopus 로고    scopus 로고
    • On the complexity of learning the kernel matrix
    • S. T. S. Becker and K. Obermayer (Eds.), Cambridge, MA: MIT Press
    • Bousquet, O., & Herrmann, D. J. L. (2003). On the complexity of learning the kernel matrix. In S. T. S. Becker and K. Obermayer (Eds.), Advances in neural information processing systems 15, 399-406. Cambridge, MA: MIT Press.
    • (2003) Advances in Neural Information Processing Systems , vol.15 , pp. 399-406
    • Bousquet, O.1    Herrmann, D.J.L.2
  • 7
    • 84898956003 scopus 로고    scopus 로고
    • Kernel design using boosting
    • S. T. S. Becker and K. Obermayer (Eds.). Cambridge, MA: MIT Press
    • Crammer, K., Keshet, J., & Singer, Y. (2003). Kernel design using boosting. In S. T. S. Becker and K. Obermayer (Eds.), Advances in neural information processing systems 15, 537-544. Cambridge, MA: MIT Press.
    • (2003) Advances in Neural Information Processing Systems , vol.15 , pp. 537-544
    • Crammer, K.1    Keshet, J.2    Singer, Y.3
  • 10
    • 0036643063 scopus 로고    scopus 로고
    • Structural modelling with sparse kernels
    • Gunn, S., & Kandola, J. (2002). Structural modelling with sparse kernels. Machine Learning, 48, 137-163.
    • (2002) Machine Learning , vol.48 , pp. 137-163
    • Gunn, S.1    Kandola, J.2
  • 12
    • 0033225865 scopus 로고    scopus 로고
    • An introduction to variational methods for graphical models
    • Jordan, M., Ghahramani, Z., Jaakkola, T., & Saul, L. (1999). An introduction to variational methods for graphical models. Machine Learning, 37, 183-233.
    • (1999) Machine Learning , vol.37 , pp. 183-233
    • Jordan, M.1    Ghahramani, Z.2    Jaakkola, T.3    Saul, L.4
  • 16
    • 1542714899 scopus 로고    scopus 로고
    • Reducing the variability in cDNA microarray image processing by Bayesian inference
    • Lawrence, N. D., Milo, M., Niranjan, M., Rashbass, P., & Soullier, S. (2004). Reducing the variability in cDNA microarray image processing by Bayesian inference. Bioinformatics, 20, 518-526.
    • (2004) Bioinformatics , vol.20 , pp. 518-526
    • Lawrence, N.D.1    Milo, M.2    Niranjan, M.3    Rashbass, P.4    Soullier, S.5
  • 21
    • 0001224048 scopus 로고    scopus 로고
    • Sparse Bayesian learning and the relevance vector machine
    • Tipping, M. (2001). Sparse Bayesian learning and the relevance vector machine. Journal of Machine Learning Research, 1, 211-244.
    • (2001) Journal of Machine Learning Research , vol.1 , pp. 211-244
    • Tipping, M.1
  • 22
    • 22944446801 scopus 로고    scopus 로고
    • Efficient hyperkernel learning using second-order cone programming
    • Tsang, I. W., & Kwok, J. T. (2004). Efficient hyperkernel learning using second-order cone programming. Proceedings of the 15th European Conference on Machine Learning (pp. 453-464).
    • (2004) Proceedings of the , vol.15 , pp. 453-464
    • Tsang, I.W.1    Kwok, J.T.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.