{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/69551"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/69551","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Probabilistic Inference: Theory and Practice (Learning, Inductive, Logic, Synthesis)","abstract":"This thesis presents a system and a methodology for probabilistic learning from examples.","abstract_html":"This thesis presents a system and a methodology for probabilistic learning from examples.","abstract_has_math":false,"creators":["Lee, Won Don"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2014,"date_issued":"2014-12-15T19:25:43Z","date_published":"2014-12-15T19:25:43Z","updated_at":"2026-07-22T22:26:01Z","subjects":["Computer Science"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["(UMI)AAI8623352"],"render_values":[{"text":"(UMI)AAI8623352","href":null,"code":true}]}]},"links":{"outbound_url":"http://hdl.handle.net/2142/69551","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:creator","label":"Author","values":["Lee, Won Don"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2014-12-15T19:25:43Z","10000-01-01","1986"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Computer Science"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/69551","(UMI)AAI8623352"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["This thesis presents a system and a methodology for probabilistic learning from examples.","First, it describes a new methodology, Probabilistic Rule Generator (PRG), of variable-valued logic synthesis which can be applied effectively to noisy data. Then, an application of the methodology to the sleep stage scoring problem is presented. A method of the communication between a human expert and a machine is described next. Finally, a new system, Probabilistic Inference, which can generate concepts with limited time and/or resources is defined. It is described how PRG can be a practical tool for Probabilistic Inference.","A departure from the classical viewpoint in logic minimization, in rule-refinement, and in knowledge acquisition is reported.","Made available in DSpace on 2014-12-15T19:25:43Z (GMT). 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