ML Anthology

Mixtures of Kikuchi Approximations

Roberto Santana, Pedro Larrañaga, José Antonio Lozano
ECML-PKDD 2006 pp. 365-376
doi:10.1007/11871842_36 /ecmlpkdd/2006/santana2006ecml-mixtures/

Abstract

Mixtures of distributions concern modeling a probability distribution by a weighted sum of other distributions. Kikuchi approximations of probability distributions follow an approach to approximate the free energy of statistical systems. In this paper, we introduce the mixture of Kikuchi approximations as a probability model. We present an algorithm for learning Kikuchi approximations from data based on the expectation-maximization (EM) paradigm. The proposal is tested in the approximation of probability distributions that arise in evolutionary computation.

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Cite

Text

Santana et al. "Mixtures of Kikuchi Approximations." European Conference on Machine Learning, 2006. doi:10.1007/11871842_36

Markdown

[Santana et al. "Mixtures of Kikuchi Approximations." European Conference on Machine Learning, 2006.](https://mlanthology.org/ecmlpkdd/2006/santana2006ecml-mixtures/) doi:10.1007/11871842_36

BibTeX

@inproceedings{santana2006ecml-mixtures,
  title     = {{Mixtures of Kikuchi Approximations}},
  author    = {Santana, Roberto and Larrañaga, Pedro and Lozano, José Antonio},
  booktitle = {European Conference on Machine Learning},
  year      = {2006},
  pages     = {365-376},
  doi       = {10.1007/11871842_36},
  url       = {https://mlanthology.org/ecmlpkdd/2006/santana2006ecml-mixtures/}
}