Classification of Ordinal Data
| dc.creator | Cardoso, Jaime S. | |
| dc.date | 2006-05-26 | |
| dc.date.accessioned | 2026-07-07T07:09:34Z | |
| dc.date.available | 2026-07-07T07:09:34Z | |
| dc.description | Classification of ordinal data is one of the most important tasks of relation learning. In this thesis a novel framework for ordered classes is proposed. The technique reduces the problem of classifying ordered classes to the standard two-class problem. The introduced method is then mapped into support vector machines and neural networks. Compared with a well-known approach using pairwise objects as training samples, the new algorithm has a reduced complexity and training time. A second novel model, the unimodal model, is also introduced and a parametric version is mapped into neural networks. Several case studies are presented to assert the validity of the proposed models. | |
| dc.description | 62 pages, MSc thesis | |
| dc.identifier | https://arxiv.org/abs/cs/0605123 | |
| dc.identifier | http://arxiv.org/abs/cs/0605123 | |
| dc.identifier.uri | http://salesiana.dossiersoluciones.com/handle/123456789/111117 | |
| dc.subject | Artificial Intelligence | |
| dc.title | Classification of Ordinal Data | |
| dc.type | text |