Learning from Exemplars and Prototypes in Machine Learning and
  Psychology
  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.
