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.
