Part of Advances in Neural Information Processing Systems 9 (NIPS 1996)
Mark Mathieson
The classes in classification tasks often have a natural ordering, and the training and testing examples are often incomplete. We propose a non(cid:173) linear ordinal model for classification into ordered classes. Predictive, simulation-based approaches are used to learn from past and classify fu(cid:173) ture incomplete examples. These techniques are illustrated by making prognoses for patients who have suffered severe head injuries.