Multivariate statistical pattern recognition with nonreduced dimensionality

Jiří Grim

Kybernetika (1986)

  • Volume: 22, Issue: 2, page 142-157
  • ISSN: 0023-5954

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Grim, Jiří. "Multivariate statistical pattern recognition with nonreduced dimensionality." Kybernetika 22.2 (1986): 142-157. <http://eudml.org/doc/28177>.

@article{Grim1986,
author = {Grim, Jiří},
journal = {Kybernetika},
keywords = {feature selection problem; approximation of mixtures; classification; reduction of dimensionality; class-conditional distributions; maximum- likelihood estimates; EM algorithm; Bernoulli mixtures},
language = {eng},
number = {2},
pages = {142-157},
publisher = {Institute of Information Theory and Automation AS CR},
title = {Multivariate statistical pattern recognition with nonreduced dimensionality},
url = {http://eudml.org/doc/28177},
volume = {22},
year = {1986},
}

TY - JOUR
AU - Grim, Jiří
TI - Multivariate statistical pattern recognition with nonreduced dimensionality
JO - Kybernetika
PY - 1986
PB - Institute of Information Theory and Automation AS CR
VL - 22
IS - 2
SP - 142
EP - 157
LA - eng
KW - feature selection problem; approximation of mixtures; classification; reduction of dimensionality; class-conditional distributions; maximum- likelihood estimates; EM algorithm; Bernoulli mixtures
UR - http://eudml.org/doc/28177
ER -

References

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  1. L. E. Baum T. Petrie G. Soules, N. Weiss, A maximization technique occurring in the statistical analysis of probabilistic functions of Markov chains, Ann. Math. Statist. 41 (1970), 164-171. (1970) MR0287613
  2. A. P. Dempster N. M. Laird, and D. B. Rubin, Maximum likelihood from incomplete data via the EM algorithm, J. Roy. Statist. Soc. B 39 (1977), 1 - 38. (1977) MR0501537
  3. J. Grim, An algorithm for maximizing a finite sum of positive functions and its application to cluster analysis, Problems Control Inform. Theory 10 (1981), 427-437. (1981) Zbl0476.65100MR0643728
  4. J. Grim, On numerical evaluation of maximum-likelihood estimates for finite mixtures of distributions, Kybernetika 18 (1982), 173-190. (1982) Zbl0489.62028MR0680154
  5. J. Grim, Application of finite mixtures to multivariate statistical pattern recognition, In: Proceedings of DIANA Conf. held in Liblice near Prague, September 27-October 1, 1982. (1982) 
  6. J. Grim, Design and optimization of multilevel homogeneous structures for multivariate pattern recognition, In: Proc. Fourth Formator Symposium (J. Beneš, L. Bakule, eds.), Academia, Prague 1983, pp. 223-240. (1983) Zbl0533.62071MR0726960
  7. J. Grim, On structural approximating multivariate discrete probability distributions, Kybernetika 20 (1984), 1-17. (1984) Zbl0547.62034MR0741419
  8. S. Kullback, Information Theory and Statistics, Dover, New York 1968. (1968) MR1461541
  9. M. A. G. Mattoso Maia, M. C. Fairhurst, On the use of I-divergence for generating distribution approximations, IEEE Trans. Pattern Anal, and Mach. Intel. PAMI-5 (1983), 661-664. (1983) Zbl0527.62027
  10. R. A. Redner, H. F. Walker, Mixture densities, maximum likelihood and the EM algorithm, SIAM Review 26 (1984), 195-239. (1984) Zbl0536.62021MR0738930
  11. M. I. Shlezinger, Relation between learning and self-learning in pattern recognition, (in Russian). Kibernetika (Kiev) (1968), 2, 81-88. (1968) 
  12. C. F. J. Wu, On the convergence properties of the EM algorithm, Ann. Statist. 11 (1983), 95-103. (1983) Zbl0517.62035MR0684867

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