Abstract
We outline a possibilistic learning method for structure identification from a database of samples. In comparison to the construction of Bayesian belief networks, the proposed framework has some advantages, namely the explicit consideration of imprecise data, and the realization of a controlled form of information compression in order to increase the efficiency of the learning strategy as well as approximate reasoning using local propagation techniques.
Our learning method has been applied to reconstruct a non-singly connected network of 22 nodes and 22 arcs without the need of any a priori supplied node ordering.
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Gebhardt, J., Kruse, R. (1995). Some Notes on Possibilistic Learning. In: Della Riccia, G., Kruse, R., Viertl, R. (eds) Proceedings of the ISSEK94 Workshop on Mathematical and Statistical Methods in Artificial Intelligence. International Centre for Mechanical Sciences, vol 363. Springer, Vienna. https://doi.org/10.1007/978-3-7091-2690-5_2
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DOI: https://doi.org/10.1007/978-3-7091-2690-5_2
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