Bayesian Approach to Neuro-Rough Models
| dc.creator | Marwala, Tshilidzi | |
| dc.creator | Crossingham, Bodie | |
| dc.date | 2007-05-06 | |
| dc.date | 2007-08-28 | |
| dc.date.accessioned | 2026-07-07T08:25:41Z | |
| dc.date.available | 2026-07-07T08:25:41Z | |
| dc.description | This paper proposes a neuro-rough model based on multi-layered perceptron and rough set. The neuro-rough model is then tested on modelling the risk of HIV from demographic data. The model is formulated using Bayesian framework and trained using Monte Carlo method and Metropolis criterion. When the model was tested to estimate the risk of HIV infection given the demographic data it was found to give the accuracy of 62%. The proposed model is able to combine the accuracy of the Bayesian MLP model and the transparency of Bayesian rough set model. | |
| dc.description | 24 pages, 5 figures, 1 table | |
| dc.identifier | https://arxiv.org/abs/0705.0761 | |
| dc.identifier | http://arxiv.org/abs/0705.0761 | |
| dc.identifier.uri | http://salesiana.dossiersoluciones.com/handle/123456789/136700 | |
| dc.subject | Artificial Intelligence | |
| dc.title | Bayesian Approach to Neuro-Rough Models | |
| dc.type | text |