Editing Supervised learning (section)

==Generalizations==

[[File:Task-guidance.png|thumb|300px|Tendency for a task to employ supervised vs. unsupervised methods. Task names straddling circle boundaries is intentional. It shows that the classical division of imaginative tasks (left) employing unsupervised methods is blurred in today's learning schemes.]]There are several ways in which the standard supervised learning problem can be generalized:
* [[Semi-supervised learning]] or [[weak supervision]]: the desired output values are provided only for a subset of the training data. The remaining data is unlabeled or imprecisely labeled.
* [[Active learning (machine learning)|Active learning]]: Instead of assuming that all of the training examples are given at the start, active learning algorithms interactively collect new examples, typically by making queries to a human user. Often, the queries are based on unlabeled data, which is a scenario that combines semi-supervised learning with active learning.
* [[Structured prediction]]: When the desired output value is a complex object, such as a [[parse tree]] or a labeled graph, then standard methods must be extended.
* [[Learning to rank]]: When the input is a set of objects and the desired output is a ranking of those objects, then again the standard methods must be extended.