Concept learning is when the goal of learing is to allocate input into one of a number of distinct classifications. Often concept learning is applied to a binary condition: 'in category' vs 'not in category', and some algorithms are typically expressed in this binary form. Examples of concept learning include symbolic algorthms such as version spaces and ID3, and also sub-symbolic techniques including many kinds of neural network and swarm computing.
Used in glossary entries: ID3, neural network, swarm computing, version-space algorithm