In many real-world prediction tasks, class labels include information about the relative
ordering between labels, which is not captured by commonly-used loss functions such as …

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 …

Learning to rank for Information Retrieval (IR) is a task to automatically construct a ranking
model using training data, such that the model can sort new objects according to their …

Support Vector Machines: Optimization Based Theory, Algorithms, and Extensions presents
an accessible treatment of the two main components of support vector machines (SVMs) …

This chapter gives a general introduction to learning for ranking aggregation. Ranking
aggregation is aimed at combining multiple rankings into a single ranking, which is better …

The paper is concerned with applying learning to rank to document retrieval. Ranking SVM
is a typical method of learning to rank. We point out that there are two factors one must …

We present a probabilistic kernel approach to ordinal regression based on Gaussian
processes. A threshold model that generalizes the probit function is used as the likelihood …

This paper is concerned with learning to rank for information retrieval (IR). Ranking is the
central problem for information retrieval, and employing machine learning techniques to …

In this letter, we propose two new support vector approaches for ordinal regression, which
optimize multiple thresholds to define parallel discriminant hyperplanes for the ordinal …

Ordinal regression (OR-also known as ordinal classification) has received increasing
attention in recent times, due to its importance in IR applications such as learning to rank …