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Learning overhypotheses with hierarchical Bayesian models

Learning overhypotheses with hierarchical Bayesian models Inductive learning is impossible without overhypotheses, or constraints on the hypotheses considered by the learner. Some of these overhypotheses must be innate, but we suggest that hierarchical Bayesian models can help to explain how the rest are acquired. To illustrate this claim, we develop models that acquire two kinds of overhypotheses – overhypotheses about feature variability (e.g. the shape bias in word learning) and overhypotheses about the grouping of categories into ontological kinds like objects and substances. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Developmental Science Wiley

Learning overhypotheses with hierarchical Bayesian models

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References (49)

Publisher
Wiley
Copyright
Copyright © 2007 Wiley Subscription Services, Inc., A Wiley Company
ISSN
1363-755X
eISSN
1467-7687
DOI
10.1111/j.1467-7687.2007.00585.x
pmid
17444972
Publisher site
See Article on Publisher Site

Abstract

Inductive learning is impossible without overhypotheses, or constraints on the hypotheses considered by the learner. Some of these overhypotheses must be innate, but we suggest that hierarchical Bayesian models can help to explain how the rest are acquired. To illustrate this claim, we develop models that acquire two kinds of overhypotheses – overhypotheses about feature variability (e.g. the shape bias in word learning) and overhypotheses about the grouping of categories into ontological kinds like objects and substances.

Journal

Developmental ScienceWiley

Published: May 1, 2007

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