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 …

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 …

Ordinal regression (OR) aims to construct the classifier from data with ordered class labels.
At present, most of the OR methods consider the OR problem as a single learning task and …

Recently, distance metric learning (DML) has attracted much attention in image retrieval, but
most previous methods only work for image classification and clustering tasks. In this brief …

As a special case of multi-classification, ordinal regression (also known as ordinal
classification) is a popular method to tackle the multi-class problems with samples marked …

Estimation of pain intensity from facial expressions captured in videos has an immense
potential for health care applications. Given the challenges related to subjective variations of …

Semi-supervised ordinal regression (S 2 OR) problems are ubiquitous in real-world
applications, where only a few ordered instances are labeled and massive instances remain …

Images usually have specific spatial structures, and related researches have shown that
these structures can contribute to the establishment of more effective classification …

Semi-supervised ordinal regression (S2OR) has been recognized as a valuable technique
to improve the performance of the ordinal regression (OR) model by leveraging available …

Ordinal regression problem arises in situations where examples are rated in an ordinal
scale. In practice, labeled ordinal data are difficult to obtain while unlabeled ordinal data are …