Analyzing human feature learning as nonparametric Bayesian inference

Joseph L. Austerweil, Thomas L. Griffiths

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

13 Scopus citations

Abstract

Almost all successful machine learning algorithms and cognitive models require powerful representations capturing the features that are relevant to a particular problem. We draw on recent work in nonparametric Bayesian statistics to define a rational model of human feature learning that forms a featural representation from raw sensory data without pre-specifying the number of features. By comparing how the human perceptual system and our rational model use distributional and category information to infer feature representations, we seek to identify some of the forces that govern the process by which people separate and combine sensory primitives to form features.

Original languageEnglish (US)
Title of host publicationAdvances in Neural Information Processing Systems 21 - Proceedings of the 2008 Conference
Pages97-104
Number of pages8
StatePublished - Dec 1 2009
Externally publishedYes
Event22nd Annual Conference on Neural Information Processing Systems, NIPS 2008 - Vancouver, BC, Canada
Duration: Dec 8 2008Dec 11 2008

Publication series

NameAdvances in Neural Information Processing Systems 21 - Proceedings of the 2008 Conference

Other

Other22nd Annual Conference on Neural Information Processing Systems, NIPS 2008
CountryCanada
CityVancouver, BC
Period12/8/0812/11/08

All Science Journal Classification (ASJC) codes

  • Information Systems

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    Austerweil, J. L., & Griffiths, T. L. (2009). Analyzing human feature learning as nonparametric Bayesian inference. In Advances in Neural Information Processing Systems 21 - Proceedings of the 2008 Conference (pp. 97-104). (Advances in Neural Information Processing Systems 21 - Proceedings of the 2008 Conference).