Instance-based learning: Difference between revisions

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In [[machine learning]], '''instance-based learning''' (sometimes called '''memory-based learning'''<ref>{{cite book |author1=Walter Daelemans |authorlink1=Walter Daelemans |author2=Antal van den Bosch |authorlink2=Antal van den Bosch |year=2005 |title=Memory-Based Language Processing |publisher=Cambridge University Press}}</ref>) is a family of learning algorithms that, instead of performing explicit generalization, comparescompare new problem instances with instances seen in training, which have been stored in memory. Because computation is postponed until a new instance is observed, these algorithms are sometimes referred to as "lazy."<ref name="mitchell"></ref>

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>