Name
Abstract | ||
|---|---|---|
This paper investigates a way of using background knowledge in the rule discovery process. This technique is based on Generalization Distribution Table (GDT for short), in which the probabilistic relationships between concepts and instances over discrete domains are represented. We describe how to use background knowledge as a bias to adjust the prior distribution so that the better knowledge can be discovered. |
Year | DOI | Venue |
|---|---|---|
2000 | 10.1007/3-540-45372-5_86 | PKDD |
Keywords | Field | DocType |
better knowledge,rule discovery process,probabilistic relationship,discrete domain,generalization distribution,prior distribution | Information processing,Computer science,Artificial intelligence,Probabilistic logic,Prior probability,Business process discovery,Uncertainty handling,Knowledge acquisition,Machine learning | Conference |
Volume | ISSN | ISBN |
1910 | 0302-9743 | 3-540-41066-X |
Citations | PageRank | References |
2 | 0.41 | 4 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ning Zhong | 1 | 2907 | 300.63 |
| Juzhen Dong | 2 | 214 | 17.05 |
| Setsuo Ohsuga | 3 | 960 | 222.02 |