Learning systems of concepts with an infinite relational model. Export

@inproceedings{citeulike:773292, abstract = {Relationships between concepts account for a large proportion of semantic knowledge. We present a nonparametric Bayesian model that discovers systems of related concepts. Given data involving several sets of entities, our model discovers the kinds of entities in each set and the relations between kinds that are possible or likely. We apply our approach to four problems: clustering objects and features, learning ontologies, discovering kinship systems, and discovering structure in political data.}, author = {Kemp, C. and Tenenbaum, J. B. and Griffiths, T. L. and Yamada, T. and Ueda, N.}, booktitle = {AAAI}, citeulike-article-id = {773292}, citeulike-linkout-0 = {http://web.mit.edu/cocosci/josh.html}, citeulike-linkout-1 = {http://web.mit.edu/cocosci/Papers/Kemp-etal-AAAI06.pdf}, keywords = {abstraction, causal\_relation, induction, learning}, posted-at = {2007-10-01 11:13:27}, priority = {5}, title = {Learning systems of concepts with an infinite relational model.}, url = {http://web.mit.edu/cocosci/josh.html}, year = {2006} }

TY - CONF ID - citeulike:773292 L3 - citeulike-article-id:773292 N2 - Relationships between concepts account for a large proportion of semantic knowledge. We present a nonparametric Bayesian model that discovers systems of related concepts. Given data involving several sets of entities, our model discovers the kinds of entities in each set and the relations between kinds that are possible or likely. We apply our approach to four problems: clustering objects and features, learning ontologies, discovering kinship systems, and discovering structure in political data. TI - Learning systems of concepts with an infinite relational model. T2 - AAAI KW - abstraction KW - causal_relation KW - induction KW - learning AU - Kemp, C AU - Tenenbaum, JB AU - Griffiths, TL AU - Yamada, T AU - Ueda, N PY - 2006/// UR - http://web.mit.edu/cocosci/josh.html ER -