{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/21646"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/21646","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Seer: Maximum likelihood regression for learning-speed curves","abstract":"Restriction data tranferred 2014-07-01T11:24:02-05:00 Original Data Group with Access UIUC Users [automated] Release Date: none Reason: ETDs are only available to UIUC Users without author permission","abstract_html":"Restriction data tranferred 2014-07-01T11:24:02-05:00 Original Data Group with Access UIUC Users [automated] Release Date: none Reason: ETDs are only available to UIUC Users without author permission","abstract_has_math":false,"creators":["Kadie, Carl Myers"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Wilkins, David C."],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2011,"date_issued":"2011-05-07T13:14:54Z","date_published":"2011-05-07T13:14:54Z","updated_at":"2026-07-22T22:25:18Z","subjects":["Statistics","Artificial Intelligence","Computer Science"],"languages":["eng"],"rights":["Copyright 1995 Kadie, Carl Myers"],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["AAI9624379","(UMI)AAI9624379"],"render_values":[{"text":"AAI9624379","href":null,"code":true},{"text":"(UMI)AAI9624379","href":null,"code":true}]}]},"links":{"outbound_url":"http://hdl.handle.net/2142/21646","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Wilkins, David C."]},{"key":"dc:creator","label":"Author","values":["Kadie, Carl Myers"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2011-05-07T13:14:54Z","10000-01-01","1995"]},{"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":["Statistics","Artificial Intelligence","Computer Science"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 1995 Kadie, Carl Myers"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["AAI9624379","(UMI)AAI9624379","http://hdl.handle.net/2142/21646"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Restriction data tranferred 2014-07-01T11:24:02-05:00 Original Data Group with Access UIUC Users [automated] Release Date: none Reason: ETDs are only available to UIUC Users without author permission","ETDs are only available to UIUC Users without author permission","U of I Only","The research presented here focuses on modeling machine-learning performance. The thesis introduces Seer, a system that generates empirical observations of classification-learning performance and then uses those observations to create statistical models. The models can be used to predict the number of training examples needed to achieve a desired level and the maximum accuracy possible given an unlimited number of training examples. Seer advances the state of the art with (1) models that embody the best constraints for classification learning and most useful parameters, (2) algorithms that efficiently find maximum-likelihood models, and (3) a demonstration on real-world data from three domains of a practicable application of such modeling.","The first part of the thesis gives an overview of the requirements for a good maximum-likelihood model of classification-learning performance. Next, reasonable design choices for such models are explored. Selection among such models is a task of nonlinear programming, but by exploiting appropriate problem constraints, the task is reduced to a nonlinear regression task that can be solved with an efficient iterative algorithm. The latter part of the thesis describes almost 100 experiments in the domains of soybean disease, heart disease, and audiological problems. The tests show that Seer is excellent at characterizing learning-performance and that it seems to be as good as possible at predicting learning performance. Finally, recommendations for choosing a regression model for a particular situation are made and directions for further research are identified.","Made available in DSpace on 2011-05-07T13:14:54Z (GMT). No. of bitstreams: 2 license.txt: 4922 bytes, checksum: 910b249b4beec47e7ab768910c8f966f (MD5) 9624379.pdf: 3608460 bytes, checksum: c424c548141c546593755582640c3792 (MD5) Previous issue date: 1995","Item marked as restricted to the 'UIUC Users [automated]' Group (id=2) by Howard Ding (hding2@illinois.edu) on 2011-05-07T14:52:15Z Item is restricted indefinitely."]},{"key":"dc:title","label":"Title","values":["Seer: Maximum likelihood regression for learning-speed curves"]}]}],"canonical_facts":{"dc:contributor":["Wilkins, David C."],"dc:creator":["Kadie, Carl Myers"],"dc:date":["2011-05-07T13:14:54Z","10000-01-01","1995"],"dc:description":["Restriction data tranferred 2014-07-01T11:24:02-05:00 Original Data Group with Access UIUC Users [automated] Release Date: none Reason: ETDs are only available to UIUC Users without author permission","ETDs are only available to UIUC Users without author permission","U of I Only","The research presented here focuses on modeling machine-learning performance. The thesis introduces Seer, a system that generates empirical observations of classification-learning performance and then uses those observations to create statistical models. The models can be used to predict the number of training examples needed to achieve a desired level and the maximum accuracy possible given an unlimited number of training examples. Seer advances the state of the art with (1) models that embody the best constraints for classification learning and most useful parameters, (2) algorithms that efficiently find maximum-likelihood models, and (3) a demonstration on real-world data from three domains of a practicable application of such modeling.","The first part of the thesis gives an overview of the requirements for a good maximum-likelihood model of classification-learning performance. Next, reasonable design choices for such models are explored. Selection among such models is a task of nonlinear programming, but by exploiting appropriate problem constraints, the task is reduced to a nonlinear regression task that can be solved with an efficient iterative algorithm. The latter part of the thesis describes almost 100 experiments in the domains of soybean disease, heart disease, and audiological problems. The tests show that Seer is excellent at characterizing learning-performance and that it seems to be as good as possible at predicting learning performance. Finally, recommendations for choosing a regression model for a particular situation are made and directions for further research are identified.","Made available in DSpace on 2011-05-07T13:14:54Z (GMT). No. of bitstreams: 2 license.txt: 4922 bytes, checksum: 910b249b4beec47e7ab768910c8f966f (MD5) 9624379.pdf: 3608460 bytes, checksum: c424c548141c546593755582640c3792 (MD5) Previous issue date: 1995","Item marked as restricted to the 'UIUC Users [automated]' Group (id=2) by Howard Ding (hding2@illinois.edu) on 2011-05-07T14:52:15Z Item is restricted indefinitely."],"dc:identifier":["AAI9624379","(UMI)AAI9624379","http://hdl.handle.net/2142/21646"],"dc:language":["eng"],"dc:rights":["Copyright 1995 Kadie, Carl Myers"],"dc:subject":["Statistics","Artificial Intelligence","Computer Science"],"dc:title":["Seer: Maximum likelihood regression for learning-speed curves"],"dc:type":["text"],"thesis:degree_discipline":["Computer Science"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Ph.D."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:25:18Z"}