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Collective Mining of Bayesian Networks from Distributed Heterogeneous Data

โœ Scribed by R. Chen; K. Sivakumar; H. Kargupta


Publisher
Springer-Verlag
Year
2004
Tongue
English
Weight
276 KB
Volume
6
Category
Article
ISSN
0219-1377

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Existing data sets of cases can signiยฎcantly reduce the knowledge engineering eort required to parameterize Bayesian networks. Unfortunately, when a data set is small, many conditioning cases are represented by too few or no data records and they do not oer sucient basis for learning conditional pro