Name
Abstract | ||
|---|---|---|
The power of belief networks lies in its connective edges where the influences are bidirectional. While Bayesian methods capture bidirectional influences, we propose a simpler and faster method of inferencing from nodal observations that uses bidirectional fuzzy influences that are propagated via fuzzy set membership functions. We need neither the conditional probability tables nor constraining mathematical structure that make inferencing NP-hard. |
Year | Venue | Keywords |
|---|---|---|
2002 | COMPUTER APPLICATIONS IN INDUSTRY AND ENGINEERING | fuzzy set,belief network,bayesian method,conditional probability table,membership function |
Field | DocType | Citations |
Fuzzy classification,Defuzzification,Fuzzy set operations,Computer science,Fuzzy measure theory,Fuzzy set,Artificial intelligence,Fuzzy number,Type-2 fuzzy sets and systems,Membership function,Machine learning,Distributed computing | Conference | 5 |
PageRank | References | Authors |
0.67 | 5 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Carl G. Looney | 1 | 198 | 21.58 |
| Lily R. Liang | 2 | 143 | 11.40 |