Name
Title | ||
|---|---|---|
Structure space of Bayesian networks is dramatically reduced by subdividing it in sub-networks |
Abstract | ||
|---|---|---|
Currently, Bayesian Networks (BNs) have become one of the most complete, self-sustained and coherent formalisms used for knowledge acquisition, representation and application through computer systems. However, learning of BNs structures from data has been shown to be an NP-hard problem. It has turned out to be one of the most exciting challenges in machine learning. In this context, the present work's major objective lies in setting up a further solution conceived to be a remedy for the intricate algorithmic complexity imposed during the learning of BN-structure with a massively-huge data backlog. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1016/j.cam.2015.02.055 | Journal of Computational and Applied Mathematics |
Keywords | Field | DocType |
modeling,bayesian network | Algorithmic learning theory,Mathematical optimization,Structure learning,Bayesian network,Structure space,Rotation formalisms in three dimensions,Algorithmic complexity,Mathematics,Knowledge acquisition | Journal |
Volume | Issue | ISSN |
287 | C | 0377-0427 |
Citations | PageRank | References |
1 | 0.35 | 9 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Heni Bouhamed | 1 | 6 | 2.16 |
| Afif Masmoudi | 2 | 50 | 10.25 |
| Thierry Lecroq | 3 | 662 | 58.52 |
| Ahmed Riadh Rebai | 4 | 45 | 8.65 |