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Volumn 2, Issue , 2003, Pages 672-679

Optimization with EM and Expectation-Conjugate-Gradient

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; CONVERGENCE OF NUMERICAL METHODS; FUNCTIONS; MATHEMATICAL MODELS; MAXIMUM LIKELIHOOD ESTIMATION; OPTIMIZATION; PATTERN RECOGNITION; PROBABILISTIC LOGICS;

EID: 1942420675     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (129)

References (19)
  • 1
    • 21144482155 scopus 로고
    • The performance of standard and hybrid EM algorithms for ML estimates of the normal mixture model with censoring
    • S.E. Atkinson. The performance of standard and hybrid EM algorithms for ML estimates of the normal mixture model with censoring. J. of Stat. Computation and Simulation, 44, 1992.
    • (1992) J. of Stat. Computation and Simulation , vol.44
    • Atkinson, S.E.1
  • 4
    • 80052422112 scopus 로고    scopus 로고
    • On computing the largest fraction of missing information for the EM algorithm and the worst linear function for data augmentation
    • University of Washington
    • Chris Fraley. On computing the largest fraction of missing information for the EM algorithm and the worst linear function for data augmentation. Technical report, University of Washington
    • Technical Report
    • Fraley, C.1
  • 5
    • 1942482179 scopus 로고    scopus 로고
    • LBNL and UC Santa Cruz
    • GENIE gene data set. LBNL and UC Santa Cruz, http://www.fruitfly.org/ sequence.
    • GENIE Gene Data Set
  • 7
    • 0009568899 scopus 로고    scopus 로고
    • Acceleration of the EM algorithm by using quasi-newton methods
    • Mortaza Jamshidian and Robert I. Jennrich. Acceleration of the EM algorithm by using quasi-newton methods. J. of the RS Society series B, 49, 1997.
    • (1997) J. of the RS Society Series B , vol.49
    • Jamshidian, M.1    Jennrich, R.I.2
  • 10
    • 0002788893 scopus 로고    scopus 로고
    • A view of the EM algorithm that justifies incremental, sparse and other variants
    • M. I. Jordan, editor, Kluwer Academic Press
    • R. M. Neal and G. E. Hinton. A view of the EM algorithm that justifies incremental, sparse and other variants. In M. I. Jordan, editor, Learning in Graphical Models, pages 355-368. Kluwer Academic Press, 1998.
    • (1998) Learning in Graphical Models , pp. 355-368
    • Neal, R.M.1    Hinton, G.E.2
  • 11
    • 0021404166 scopus 로고
    • Mixture densities, maximum likelihood and the EM algorithm
    • April
    • Richard A. Redner and Homer F. Walker. Mixture densities, maximum likelihood and the EM algorithm. SIAM Review, 26(2):195-239, April 1984.
    • (1984) SIAM Review , vol.26 , Issue.2 , pp. 195-239
    • Redner, R.A.1    Walker, H.F.2
  • 15
    • 0033556788 scopus 로고    scopus 로고
    • Mixtures of probabilistic principal component analysers
    • M. E. Tipping and C. M. Bishop. Mixtures of probabilistic principal component analysers. Neural Computation, 11(2):443-482, 1999.
    • (1999) Neural Computation , vol.11 , Issue.2 , pp. 443-482
    • Tipping, M.E.1    Bishop, C.M.2
  • 17
    • 0032492432 scopus 로고    scopus 로고
    • Independent component filters of natural images compared with simple cells in primary visual cortex
    • J. H. van Hateren and A. van der Schaaf. Independent component filters of natural images compared with simple cells in primary visual cortex. In Proceedings of the Royal Society of London, pages 359-366, 1998.
    • (1998) Proceedings of the Royal Society of London , pp. 359-366
    • Van Hateren, J.H.1    Van Der Schaaf, A.2
  • 18
    • 2342533082 scopus 로고    scopus 로고
    • On convergence properties of the Em algorithm for Gaussian mixtures
    • L. Xu and M. I. Jordan. On convergence properties of the EM algorithm for Gaussian mixtures. Neural Computation, 8(1):129-151, 1996.
    • (1996) Neural Computation , vol.8 , Issue.1 , pp. 129-151
    • Xu, L.1    Jordan, M.I.2
  • 19
    • 33747138721 scopus 로고    scopus 로고
    • The convex-concave computational procedure (CCCP)
    • Alan Yuille and Anand Rangarajan. The convex-concave computational procedure (CCCP). In Advances in NIPS, volume 13, 2001.
    • (2001) Advances in NIPS , vol.13
    • Yuille, A.1    Rangarajan, A.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.