Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
4638522 | Journal of Computational and Applied Mathematics | 2015 | 15 Pages |
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.
Related Topics
Physical Sciences and Engineering
Mathematics
Applied Mathematics
Authors
Heni Bouhamed, Afif Masmoudi, Thierry Lecroq, Ahmed Rebaï,