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, and hidden ‘unobservable’ variables.
This work was supported by the German Federal Department of Research and Technology, grant ITW8900A7
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Paaß, G. (1990). A Stochastic EM Learning Algorithm for Structured Probabilistic Neural Networks. In: Dorffner, G. (eds) Konnektionismus in Artificial Intelligence und Kognitionsforschung. Informatik-Fachberichte, vol 252. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-76070-9_22
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DOI: https://doi.org/10.1007/978-3-642-76070-9_22
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