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Volumn , Issue , 2005, Pages 325-332

Kernel methods for missing variables

Author keywords

[No Author keywords available]

Indexed keywords

CONCAVE-CONVEX PROCEDURE; DATA SETS; ESTIMATION PROBLEM; EXPONENTIAL FAMILY; GAUSSIAN PROCESSES; INCOMPLETE DATA; INTUITIVE PROOF; KERNEL METHODS; MARGINAL DISTRIBUTION; MISSING DATA; OPTIMIZATION METHOD; OPTIMIZATION PROBLEMS; OPTIMIZATION SCHEME;

EID: 84862623799     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (256)

References (16)
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    • Dec
    • S. Fine and K. Scheinberg. Efficient SVM training using low-rank kernel representations. Journal of Machine Learning Research, 2:243 - 264, Dec 2001. http://www.jmlr.org.
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    • Fine, S.1    Scheinberg, K.2
  • 3
    • 0001551844 scopus 로고
    • Supervised learning from incomplete data via an EM approach
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    • Z. Ghahramani and M. I. Jordan. Supervised learning from incomplete data via an EM approach. In J. D. Cowan, G. Tesauro, and J. Alspector, editors, Advances in Neural Information Processing Systems, volume 6, pages 120 - 127. Morgan Kaufmann Publishers, Inc., 1994.
    • (1994) Advances in Neural Information Processing Systems , vol.6 , pp. 120-127
    • Ghahramani, Z.1    Jordan, M.I.2
  • 4
    • 0003596421 scopus 로고    scopus 로고
    • Geomtrical foundations of asymptotic inference
    • Wiley series Wiley Interscience
    • R. E. Kass and P. W. Vos. Geomtrical Foundations of Asymptotic Inference. Wiley series in Probability and Statistics. Wiley Interscience, 1997.
    • (1997) Probability and Statistics
    • Kass, R.E.1    Vos, P.W.2
  • 10
    • 84856983285 scopus 로고    scopus 로고
    • R. Oldenbourg Verlag, München Doktorarbeit, TU Berlin. Download
    • B. Schölkopf. Support Vector Learning. R. Oldenbourg Verlag, München, 1997. Doktorarbeit, TU Berlin. Download: http://www.kernel- machines.org.
    • (1997) Support Vector Learning
    • Schölkopf, B.1
  • 13
    • 0002493574 scopus 로고    scopus 로고
    • Sparse greedy matrix approximation for machine learning
    • P. Langley, editor San Francisco Morgan Kaufmann Publishers
    • A.J. Smola and B. Schölkopf. Sparse greedy matrix approximation for machine learning. In P. Langley, editor, Proceedings of the International Conference on Machine Learning, pages 911 - 918, San Francisco, 2000. Morgan Kaufmann Publishers.
    • (2000) Proceedings of the International Conference on Machine Learning , pp. 911-918
    • Smola, A.J.1    Schölkopf, B.2
  • 14
    • 4043152487 scopus 로고    scopus 로고
    • Graphical models, exponential families, and variational inference
    • Department of Statistics, September
    • M. J. Wainwright and M. I. Jordan. Graphical models, exponential families, and variational inference. Technical Report 649, UC Berkeley, Department of Statistics, September 2003.
    • (2003) Technical Report 649, UC Berkeley
    • Wainwright, M.J.1    Jordan, M.I.2
  • 15
    • 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 and Inference in Graphical Models, pages 599 - 621. MIT Press, 1999.
    • (1999) Learning and Inference in Graphical Models , pp. 599-621
    • Williams, C.K.I.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.