Name
Abstract | ||
|---|---|---|
The paper deals with the knowledge-acquisition methods designed and tested under the FEL-EXPERT Project, which is aimed at the development of rule-based diagnostic shells. Three different approaches have been used for knowledge acquisition: pattern-recognition, decision-tree, and intensional, pure probabilistic approaches. The designed methods may be applied to a wide subclass of rule-based diagnostic systems that exploit the pseudo-Bayesian model for uncertainty handling. The experimental results are discussed. |
Year | DOI | Venue |
|---|---|---|
1991 | 10.1016/0950-7051(91)90052-4 | Knowledge-Based Systems |
Keywords | Field | DocType |
knowledge acquisition,expert systems,methods,uncertainty handling,rule-based systems | Data mining,Rule-based system,Diagnostic system,Computer science,Expert system,Exploit,Artificial intelligence,Probabilistic logic,Uncertainty handling,Machine learning,Knowledge acquisition | Journal |
Volume | Issue | ISSN |
4 | 4 | 0950-7051 |
Citations | PageRank | References |
1 | 0.47 | 4 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Vladimir Mařík | 1 | 486 | 63.00 |
| Zdeněk Kouba | 2 | 4 | 1.81 |