concept learning

Terms from Artificial Intelligence: humans at the heart of algorithms

The glossary is being gradually proof checked, but currently has many typos and misspellings.

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 algirthms such as version spaces and ID3, and also sub-symbolic technqies including many kinds of neural network and swarm computing.

Used in Chap. 3: page 31; Chap. 5: pages 63, 65; Chap. 16: pages 240, 248; Chap. 18: page 282