A nonparametric Bayesian model of multi-level category learning

Kevin R. Canini, Thomas L. Griffiths

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

5 Scopus citations

Abstract

Categories are often organized into hierarchical taxonomies, that is, tree structures where each node represents a labeled category, and a node's parent and children are, respectively, the category's supertype and subtypes. A natural question is whether it is possible to reconstruct category taxonomies in cases where we are not given explicit information about how categories are related to each other, but only a sample of observations of the members of each category. In this paper, we introduce a nonparametric Bayesian model of multi-level category learning, an extension of the hierarchical Dirichlet process (HDP) that we call the tree-HDP. We demonstrate the ability of the tree-HDP to reconstruct simulated datasets of artificial taxonomies, and show that it produces similar performance to human learners on a taxonomy inference task.

Original languageEnglish (US)
Title of host publicationAAAI-11 / IAAI-11 - Proceedings of the 25th AAAI Conference on Artificial Intelligence and the 23rd Innovative Applications of Artificial Intelligence Conference
Pages307-312
Number of pages6
StatePublished - Nov 2 2011
Externally publishedYes
Event25th AAAI Conference on Artificial Intelligence and the 23rd Innovative Applications of Artificial Intelligence Conference, AAAI-11 / IAAI-11 - San Francisco, CA, United States
Duration: Aug 7 2011Aug 11 2011

Publication series

NameProceedings of the National Conference on Artificial Intelligence
Volume1

Other

Other25th AAAI Conference on Artificial Intelligence and the 23rd Innovative Applications of Artificial Intelligence Conference, AAAI-11 / IAAI-11
CountryUnited States
CitySan Francisco, CA
Period8/7/118/11/11

All Science Journal Classification (ASJC) codes

  • Software
  • Artificial Intelligence

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  • Cite this

    Canini, K. R., & Griffiths, T. L. (2011). A nonparametric Bayesian model of multi-level category learning. In AAAI-11 / IAAI-11 - Proceedings of the 25th AAAI Conference on Artificial Intelligence and the 23rd Innovative Applications of Artificial Intelligence Conference (pp. 307-312). (Proceedings of the National Conference on Artificial Intelligence; Vol. 1).