  Machine learning is a field which studies how machines can alter and adapt
their behavior, improving their actions according to the information they are
given. This field is subdivided into multiple areas, among which the best known
are supervised learning (e.g. classification and regression) and unsupervised
learning (e.g. clustering and association rules).
  Within supervised learning, most studies and research are focused on well
known standard tasks, such as binary classification, multiclass classification
and regression with one dependent variable. However, there are many other less
known problems. These are what we generically call nonstandard supervised
learning problems. The literature about them is much more sparse, and each
study is directed to a specific task. Therefore, the definitions, relations and
applications of this kind of learners are hard to find.
  The goal of this paper is to provide the reader with a broad view on the
distinct variations of nonstandard supervised problems. A comprehensive
taxonomy summarizing their traits is proposed. A review of the common
approaches followed to accomplish them and their main applications is provided
as well.
