Definition: In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.
Input: 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 proba bility distribution, which is a special case of the distribution generated by proba bilistic inference networks. Hence both types of networks can be combined al lowing 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 'un observable' variables, and uncertain and contradictory evidence.
Output:
Integrating Probabilistic Rules into Neural Networks: A Stochastic EM Learning Algorithm