Discovering Latent Classes in Relational Data
We present a framework for learning abstract relational knowledge with the aimof explaining how people acquire intuitive theories of physical, biological, orsocial systems. Our approach is based on a generative relational model withlatent classes, and simultaneously determines the kinds of entities...
| Main Authors: | , , |
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| Language: | en_US |
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2005
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| Online Access: | http://hdl.handle.net/1721.1/30489 |
| _version_ | 1826189179351990272 |
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| author | Kemp, Charles Griffiths, Thomas L. Tenenbaum, Joshua B. |
| author_facet | Kemp, Charles Griffiths, Thomas L. Tenenbaum, Joshua B. |
| author_sort | Kemp, Charles |
| collection | MIT |
| description | We present a framework for learning abstract relational knowledge with the aimof explaining how people acquire intuitive theories of physical, biological, orsocial systems. Our approach is based on a generative relational model withlatent classes, and simultaneously determines the kinds of entities that existin a domain, the number of these latent classes, and the relations betweenclasses that are possible or likely. This model goes beyond previouspsychological models of category learning, which consider attributesassociated with individual categories but not relationships between categories.We apply this domain-general framework to two specific problems: learning thestructure of kinship systems and learning causal theories. |
| first_indexed | 2024-09-23T08:11:05Z |
| id | mit-1721.1/30489 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T08:11:05Z |
| publishDate | 2005 |
| record_format | dspace |
| spelling | mit-1721.1/304892019-04-09T17:10:20Z Discovering Latent Classes in Relational Data Kemp, Charles Griffiths, Thomas L. Tenenbaum, Joshua B. AI learning categorization relations kinship We present a framework for learning abstract relational knowledge with the aimof explaining how people acquire intuitive theories of physical, biological, orsocial systems. Our approach is based on a generative relational model withlatent classes, and simultaneously determines the kinds of entities that existin a domain, the number of these latent classes, and the relations betweenclasses that are possible or likely. This model goes beyond previouspsychological models of category learning, which consider attributesassociated with individual categories but not relationships between categories.We apply this domain-general framework to two specific problems: learning thestructure of kinship systems and learning causal theories. 2005-12-22T01:36:09Z 2005-12-22T01:36:09Z 2004-07-22 MIT-CSAIL-TR-2004-050 AIM-2004-019 http://hdl.handle.net/1721.1/30489 en_US Massachusetts Institute of Technology Computer Science and Artificial Intelligence Laboratory 12 p. 13382538 bytes 572002 bytes application/postscript application/pdf application/postscript application/pdf |
| spellingShingle | AI learning categorization relations kinship Kemp, Charles Griffiths, Thomas L. Tenenbaum, Joshua B. Discovering Latent Classes in Relational Data |
| title | Discovering Latent Classes in Relational Data |
| title_full | Discovering Latent Classes in Relational Data |
| title_fullStr | Discovering Latent Classes in Relational Data |
| title_full_unstemmed | Discovering Latent Classes in Relational Data |
| title_short | Discovering Latent Classes in Relational Data |
| title_sort | discovering latent classes in relational data |
| topic | AI learning categorization relations kinship |
| url | http://hdl.handle.net/1721.1/30489 |
| work_keys_str_mv | AT kempcharles discoveringlatentclassesinrelationaldata AT griffithsthomasl discoveringlatentclassesinrelationaldata AT tenenbaumjoshuab discoveringlatentclassesinrelationaldata |