Introduction to Relational Networks for Classification
| dc.creator | Marivate, Vukosi | |
| dc.creator | Marwala, Tshilidzi | |
| dc.date | 2008-04-29 | |
| dc.date.accessioned | 2026-07-07T09:35:54Z | |
| dc.date.available | 2026-07-07T09:35:54Z | |
| dc.description | The use of computational intelligence techniques for classification has been used in numerous applications. This paper compares the use of a Multi Layer Perceptron Neural Network and a new Relational Network on classifying the HIV status of women at ante-natal clinics. The paper discusses the architecture of the relational network and its merits compared to a neural network and most other computational intelligence classifiers. Results gathered from the study indicate comparable classification accuracies as well as revealed relationships between data features in the classification data. Much higher classification accuracies are recommended for future research in the area of HIV classification as well as missing data estimation. | |
| dc.description | 5 pages | |
| dc.identifier | https://arxiv.org/abs/0804.4682 | |
| dc.identifier | http://arxiv.org/abs/0804.4682 | |
| dc.identifier.uri | http://salesiana.dossiersoluciones.com/handle/123456789/159989 | |
| dc.subject | Machine Learning | |
| dc.title | Introduction to Relational Networks for Classification | |
| dc.type | text |