{"pk":32927,"title":"Concept Formation and Attention","subtitle":null,"abstract":"In this paper, I combine the ideas of attention from cognitive psychology with concept formation in machine learning. M y claim is that the use of attention can lead to a more efficient learning system, without sacrificing accuracy. Attention leads to a savings in efficiency because it focuses only on the relevant attributes, retrieves less information from the environment, and is therefore less costly than a system that uses every piece of information available. I present a working dgorithm for attention, built onto the Classit concept formation system, and describe results from three domains.'","language":"eng","license":{"name":"","short_name":"","text":null,"url":""},"keywords":[],"section":"Poster Presentations","is_remote":true,"remote_url":"https://escholarship.org/uc/item/6fj0c4xn","frozenauthors":[{"first_name":"John","middle_name":"H.","last_name":"Gennari","name_suffix":"","institution":"Keio University","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/32927/galley/23987/download/"}]}