Classification of Ordinal Data
<|reference_start|>Classification of Ordinal Data: 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.<|reference_end|>
@article{cardoso2006classification,
    title={Classification of Ordinal Data},
    author={Jaime S. Cardoso},
    journal={arXiv preprint arXiv:cs/0605123},
    year={2006},
    archivePrefix={arXiv},
    eprint={cs/0605123},
    primaryClass={cs.AI}
}