Learning overhypotheses with hierarchical Bayesian models Export

@article{citeulike:1242370, 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.}, author = {Kemp and Charles and Perfors and Amy and Tenenbaum and Joshua, B.}, citeulike-article-id = {1242370}, citeulike-linkout-0 = {http://www.blackwell-synergy.com/doi/abs/10.1111/j.1467-7687.2007.00585.x}, citeulike-linkout-1 = {http://dx.doi.org/10.1111/j.1467-7687.2007.00585.x}, citeulike-linkout-2 = {http://www.ingentaconnect.com/content/bpl/desc/2007/00000010/00000003/art00004}, citeulike-linkout-3 = {http://view.ncbi.nlm.nih.gov/pubmed/17444972}, citeulike-linkout-4 = {http://www.hubmed.org/display.cgi?uids=17444972}, doi = {10.1111/j.1467-7687.2007.00585.x}, issn = {1363-755X}, journal = {Developmental Science}, keywords = {bayesian, induction, learning}, month = {May}, number = {3}, pages = {307--321}, posted-at = {2007-10-01 10:15:37}, priority = {3}, publisher = {Blackwell Publishing}, title = {Learning overhypotheses with hierarchical Bayesian models}, url = {http://dx.doi.org/10.1111/j.1467-7687.2007.00585.x}, volume = {10}, year = {2007} }

TY - JOUR ID - citeulike:1242370 L3 - citeulike-article-id:1242370 N2 - 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. IS - 3 JF - Developmental Science SN - 1363-755X EP - 321 TI - Learning overhypotheses with hierarchical Bayesian models PB - Blackwell Publishing VL - 10 SP - 307 KW - bayesian KW - induction KW - learning AU - Kemp AU - Charles AU - Perfors AU - Amy AU - Tenenbaum AU - Joshua, B PY - 2007/05// UR - http://dx.doi.org/10.1111/j.1467-7687.2007.00585.x ER -