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This paper draws a parallel between similarity-based categorisation modelsdeveloped in cognitive psychology and the nearest neighbour classifier (1-NN)in machine learning. Conceived as a result of the historical rivalry betweenprototype theories (abstraction) and exemplar theories (memorisation), recentmodels of human categorisation seek a compromise in-between. Regarding thestimuli (entities to be categorised) as points in a metric space, machinelearning offers a large collection of methods to select a small, representativeand discriminative point set. These methods are known under various names:instance selection, data editing, prototype selection, prototype generation orprototype replacement. The nearest neighbour classifier is used with theselected reference set. Such a set can be interpreted as a data-drivencategorisation model. We juxtapose the models from the two fields to enablecross-referencing. We believe that both machine learning and cognitivepsychology can draw inspiration from the comparison and enrich their repertoireof similarity-based models.

Learning from Exemplars and Prototypes in Machine Learning and Psychology