Integrating Probabilistic Rules into Neural Networks: A Stochastic EM Learning Algorithm

Paass, Gerhard

Integrating Probabilistic Rules into Neural Networks: A Stochastic EM Learning Algorithm

UAI 1991

/uai/1991/paass1991uai-integrating/

Abstract

The EM-algorithm is a general procedure to get maximum likelihood estimates if part of the observations on the variables of a network are missing. In this paper a stochastic version of the algorithm is adapted to probabilistic neural networks describing the associative dependency of variables. These networks have a probability distribution, which is a special case of the distribution generated by probabilistic inference networks. Hence both types of networks can be combined allowing to integrate probabilistic rules as well as unspecified associations in a sound way. The resulting network may have a number of interesting features including cycles of probabilistic rules, hidden 'unobservable' variables, and uncertain and contradictory evidence.

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Text

Paass. "Integrating Probabilistic Rules into Neural Networks: A Stochastic EM Learning Algorithm." Conference on Uncertainty in Artificial Intelligence, 1991.

Markdown

[Paass. "Integrating Probabilistic Rules into Neural Networks: A Stochastic EM Learning Algorithm." Conference on Uncertainty in Artificial Intelligence, 1991.](https://mlanthology.org/uai/1991/paass1991uai-integrating/)

BibTeX

@inproceedings{paass1991uai-integrating,
  title     = {{Integrating Probabilistic Rules into Neural Networks: A Stochastic EM Learning Algorithm}},
  author    = {Paass, Gerhard},
  booktitle = {Conference on Uncertainty in Artificial Intelligence},
  year      = {1991},
  url       = {https://mlanthology.org/uai/1991/paass1991uai-integrating/}
}