Constructing Hierarchical Concepts via Analogical Generalization

C. Liang, K. Forbus

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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.
Original languageEnglish
Title of host publicationProceedings of the Cognitive Science Society
StatePublished - 2014
EventCogSci 2014 - Quebec City, Canada
Duration: Jul 1 2014 → …


ConferenceCogSci 2014
Period7/1/14 → …

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