Skip to main content

1998 | OriginalPaper | Buchkapitel

Probabilistic and Possibilistic Networks and How To Learn Them from Data

verfasst von : Christian Borgelt, Rudolf Kruse

Erschienen in: Computational Intelligence: Soft Computing and Fuzzy-Neuro Integration with Applications

Verlag: Springer Berlin Heidelberg

Aktivieren Sie unsere intelligente Suche um passende Fachinhalte oder Patente zu finden.

search-config
loading …

In this paper we explain in a tutorial manner the technique of reasoning in probabilistic and possibilistic network structures, which is based on the idea to decompose a multi-dimensional probability or possibility distribution and to draw inferences using only the parts of the decomposition. Since constructing probabilistic and possibilistic networks by hand can be tedious and time-consuming, we also discuss how ta learn probabilistic and possibilistic networks from a data, i.e. how to determine from a database of sample cases an appropriate decomposition of the underlying probability or possibility distribution.

Metadaten
Titel
Probabilistic and Possibilistic Networks and How To Learn Them from Data
verfasst von
Christian Borgelt
Rudolf Kruse
Copyright-Jahr
1998
Verlag
Springer Berlin Heidelberg
DOI
https://doi.org/10.1007/978-3-642-58930-0_19

Neuer Inhalt