Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Integrating Probabilistic Rules into Neural Networks: A Stochastic EM
  Learning Algorithm   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.

13
The categories of this paper are: quant-ph
22
The categories of this paper are: chao-dyn
4
The categories of this paper are: cond-mat
35
The categories of this paper are: mtrl-th
7
The categories of this paper are: hep-th
15
The categories of this paper are: nucl-ex
17
The categories of this paper are: q-fin
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The categories of this paper are: dg-ga
34
The categories of this paper are: chem-ph
18
The categories of this paper are: econ
36
The categories of this paper are: cmp-lg
27
The categories of this paper are: comp-gas
25
The categories of this paper are: patt-sol
14
The categories of this paper are: math-ph
16
The categories of this paper are: stat
26
The categories of this paper are: bayes-an