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In [[machine learning]], '''instance-based learning''' or '''memory-based learning''' is a family of learning algorithms that, instead of performing explicit generalization, compare new problem instances with instances seen in training, which have been stored in memory. Instance-based learning is a kind of [[lazy learning]].
It is called instance-based because it constructs hypotheses directly from the training instances themselves.<ref name='aima733'>[[Stuart J. Russell|Stuart Russell]] and [[Peter Norvig]] (2003). ''[[Artificial Intelligence: A Modern Approach]]'', second edition, p. 733. Prentice Hall. ISBN 0-13-080302-2</ref>
This means that the hypothesis complexity can grow with the data:<ref name='aima733'/> in the worst case, a hypothesis is a list of ''n'' training items and [[Classification (machine learning)|classification]] takes [[Big O notation|''O'']](''n''). One advantage that instance-based learning has over other methods of machine learning is its ability to adapt its model to previously unseen data. Where other methods generally require the entire set of training data to be re-examined when one instance is changed, instance-based learners may simply store a new instance or throw an old instance away.{{Citation needed|date=June 2010}}
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