Supervised learning from incomplete data via an EM approach

@inproceedings{Ghahramani1993SupervisedLF,
  title={Supervised learning from incomplete data via an EM approach},
  author={Zoubin Ghahramani and Michael I. Jordan},
  booktitle={NIPS},
  year={1993}
}
Real-world learning tasks may involve high-dimensional data sets with arbitrary patterns of missing data. In this paper we present a framework based on maximum likelihood density estimation for learning from such data set.s. VVe use mixture models for the density estimates and make two distinct appeals to the ExpectationMaximization (EM) principle (Dempster et al., 1977) in deriving a learning algorithm-EM is used both for the estimation of mixture components and for coping wit.h missing dat.a… CONTINUE READING
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Referenced Papers

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Showing 1-10 of 12 references

Soft Competitive Adaptation: Neural Network Learning Algorithms based on Fitting Statistical Mixtures

  • S. J. Nowlan
  • 1991
1 Excerpt

Mixture models : Inference and applicationsto clustering

  • K. Basford
  • Marcel Dekkel '
  • 1988

Mixture models: Inference and applications to clustering. Marcel Dekkel

  • G. McLachlan, K. Basford
  • 1988
1 Excerpt

Statistical Analysis with Mis.'ling Data

  • R.J.A. Little, D. B. Rubin
  • 1987
1 Excerpt

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