Learning hierarchical concepts is a central problem in cognitive science. This paper explores the Nearest-Merge algorithm for creating hierarchical clusters that can handle both feature-based and relational information, building on the SAGE model of analogical generalization. We describe its results on three data sets, showing that it provides reasonable fits with human data and comparable results to Bayesian models.
|Title of host publication||Proceedings of the Cognitive Science Society|
|State||Published - 2014|
|Event||CogSci 2014 - Quebec City, Canada|
Duration: Jul 1 2014 → …
|Period||7/1/14 → …|