{"pk":32971,"title":"A Continum of Induction Methods for Learning Probability Distributions with Generalization","subtitle":null,"abstract":"Probabilistic models of pattern completion have several advantages, namely, ability to handle arbitrary conceptual representations including compositional structures, and explicitness of distributional assumptions. However, a gap in the theory of induction of priors has hindered probabilistic modeling of cognitive generalization bitises. W e propose a family of methods parameterized along a value 7 that controls the degree to which the probability distribution being induced generalizes from the training set. The extremes of the 7-continuum correspond to relative frequency methods and extreme maximum entropy methods. The methods apply to a wide range of pattern representations including simple feature vectors as well as frame-like feature DAGs.","language":"eng","license":{"name":"","short_name":"","text":null,"url":""},"keywords":[],"section":"Poster Presentations","is_remote":true,"remote_url":"https://escholarship.org/uc/item/6cc7r26d","frozenauthors":[{"first_name":"Dekai","middle_name":"","last_name":"Wu","name_suffix":"","institution":"University of California at Berkeley","department":""}],"date_submitted":null,"date_accepted":null,"date_published":"1991-01-01T18:00:00Z","render_galley":null,"galleys":[{"label":"PDF","type":"pdf","path":"https://journalpub.escholarship.org/cognitivesciencesociety/article/32971/galley/24032/download/"}]}