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Machine learning is a field which studies how machines can alter and adapttheir behavior, improving their actions according to the information they aregiven. This field is subdivided into multiple areas, among which the best knownare supervised learning (e.g. classification and regression) and unsupervisedlearning (e.g. clustering and association rules).  Within supervised learning, most studies and research are focused on wellknown standard tasks, such as binary classification, multiclass classificationand regression with one dependent variable. However, there are many other lessknown problems. These are what we generically call nonstandard supervisedlearning problems. The literature about them is much more sparse, and eachstudy is directed to a specific task. Therefore, the definitions, relations andapplications of this kind of learners are hard to find.  The goal of this paper is to provide the reader with a broad view on thedistinct variations of nonstandard supervised problems. A comprehensivetaxonomy summarizing their traits is proposed. A review of the commonapproaches followed to accomplish them and their main applications is providedas well.

A snapshot on nonstandard supervised learning problems: taxonomy, relationships and methods