Ordinal regression problems are those machine learning problems where the objective is to
classify patterns using a categorical scale which shows a natural order between the labels …

Head pose estimation has attracted immense research interest recently, as its inherent
information significantly improves the performance of face-related applications such as face …

Some recent work treats age estimation as an ordinal ranking task and decomposes it into
multiple binary classifications. However, a theoretical defect lies in this type of methods: the …

Individualized health-care is gaining traction recently due to the advances in sensor
technology, edge computing and the improvement in communication technologies. In this …

Artificial neural networks (ANNs) have traditionally been seen as black-box models,
because, although they are able to find “hidden” relations between inputs and outputs with a …

The classification of patterns into naturally ordered labels is referred to as ordinal regression
or ordinal classification. Usually, this classification setting is by nature highly imbalanced …

In this paper, the well known stagewise additive modeling using a multiclass exponential
(SAMME) boosting algorithm is extended to address problems where there exists a natural …

Ordinal regression (OR) is a paradigm which learns a prediction model on the data with
ordered classes. Despite much progress in OR, the existing OR works learn the classifier …

Ordinal attribute has all the common characteristics of a nominal one but it differs from the
nominal one by having naturally ordered possible values (also called categories …

Ordinal regression is a supervised learning problem where training samples are labeled by
an ordinal scale. The ordering relation and nonmetric property of the label set distinguish it …