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arXiv · 1806.01130

Learning from Exemplars and Prototypes in Machine Learning and Psychology

Julian Zubek·Ludmila Kuncheva
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Abstract

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

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Julian Zubek, Ludmila Kuncheva. 2018-06-04. Learning from Exemplars and Prototypes in Machine Learning and Psychology. https://arxiv.org/abs/1806.01130

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