Encyclopedia of Machine Learning

2010 Edition
| Editors: Claude Sammut, Geoffrey I. Webb

Supervised Learning

Reference work entry
DOI: https://doi.org/10.1007/978-0-387-30164-8_803


Supervised learning refers to any machine learning process that learns a function from an input type to an output type using data comprising examples that have both input and output values. Two typical examples of supervised learning are  classification learning and  regression. In these cases, the output types are respectively categorical (the classes) and numeric. Supervised learning stands in contrast to  unsupervised learning, which seeks to learn structure in data, and to  reinforcement learning in which sequential decision-making policies are learned from reward with no examples of “correct” behavior.

Cross References

Copyright information

© Springer Science+Business Media, LLC 2011