Encyclopedia of Machine Learning and Data Mining

2017 Edition
| Editors: Claude Sammut, Geoffrey I. Webb

Supervised Learning

Reference work entry
DOI: https://doi.org/10.1007/978-1-4899-7687-1_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.


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© Springer Science+Business Media New York 2017