Name
Abstract | ||
|---|---|---|
Most documented Bayesian network (BN) applications have been built through knowledge elicitation from domain experts (DEs) The difficulties involved have led to growing interest in machine learning of BNs from data There is a further need for combining what can be learned from the data with what can be elicited from DEs In this paper, we propose a detailed methodology for this combination, specifically for the parameters of a BN. |
Year | DOI | Venue |
|---|---|---|
2004 | 10.1007/978-3-540-30549-1_108 | Australian Conference on Artificial Intelligence |
Keywords | Field | DocType |
bayesian network,machine learning | Computer science,Subject-matter expert,Bayesian network,Knowledge engineering,Artificial intelligence,Knowledge elicitation,Machine learning | Conference |
Volume | ISSN | ISBN |
3339 | 0302-9743 | 3-540-24059-4 |
Citations | PageRank | References |
19 | 1.11 | 8 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Owen Woodberry | 1 | 76 | 6.24 |
| Ann E. Nicholson | 2 | 692 | 88.01 |
| Kevin B. Korb | 3 | 400 | 52.03 |
| Carmel A. Pollino | 4 | 136 | 9.08 |