{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/80814"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/80814","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Learning in High Dimensional Spaces: Applications, Theory, and Algorithms","abstract":"The theoretical results are used to extend the existing learning algorithms. Based on the results from probabilistic classifiers, we have proposed an improved learning algorithm for HMMs which attempts to learn a maximum likelihood classifier under the minimum conditional entropy prior. A margin distribution optimization algorithm is proposed based on the results on generalization bounds and our results show that this new algorithm is better than the existing SVM and boosting algorithms.","abstract_html":"The theoretical results are used to extend the existing learning algorithms. Based on the results from probabilistic classifiers, we have proposed an improved learning algorithm for HMMs which attempts to learn a maximum likelihood classifier under the minimum conditional entropy prior. A margin distribution optimization algorithm is proposed based on the results on generalization bounds and our results show that this new algorithm is better than the existing SVM and boosting algorithms.","abstract_has_math":false,"creators":["Ashutosh"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Electrical Engineering","degree_department":null,"school":null,"contributors":["Huang, Thomas S.","Roth, Dan"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2015,"date_issued":"2015-09-25T20:08:17Z","date_published":"2015-09-25T20:08:17Z","updated_at":"2026-07-22T22:26:15Z","subjects":["Artificial Intelligence"],"languages":["eng"],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["(MiAaPQ)AAI3086006"],"render_values":[{"text":"(MiAaPQ)AAI3086006","href":null,"code":true}]}]},"links":{"outbound_url":"http://hdl.handle.net/2142/80814","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Huang, Thomas S.","Roth, Dan"]},{"key":"dc:creator","label":"Author","values":["Ashutosh"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2015-09-25T20:08:17Z","10000-01-01","2003"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical Engineering"]},{"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":["Artificial Intelligence"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["eng"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/80814","(MiAaPQ)AAI3086006"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["The theoretical results are used to extend the existing learning algorithms. Based on the results from probabilistic classifiers, we have proposed an improved learning algorithm for HMMs which attempts to learn a maximum likelihood classifier under the minimum conditional entropy prior. A margin distribution optimization algorithm is proposed based on the results on generalization bounds and our results show that this new algorithm is better than the existing SVM and boosting algorithms.","Made available in DSpace on 2015-09-25T20:08:17Z (GMT). No. of bitstreams: 2 license.txt: 4848 bytes, checksum: 96035ab3f5e1c23cc7138a224ce498bd (MD5) 3086006.pdf: 6020190 bytes, checksum: 67c25f26bdc4812528c857bef059d8ae (MD5) Previous issue date: 2003","Embargo set by: Seth Robbins for item 82096 Lift date: Forever Reason: Restricted to the U of I community idenfinitely during batch ingest of legacy ETDs","Restricted to the U of I community idenfinitely during batch ingest of legacy ETDs","U of I Only","110 p.","Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2003."]},{"key":"dc:title","label":"Title","values":["Learning in High Dimensional Spaces: Applications, Theory, and Algorithms"]}]}],"canonical_facts":{"dc:contributor":["Huang, Thomas S.","Roth, Dan"],"dc:creator":["Ashutosh"],"dc:date":["2015-09-25T20:08:17Z","10000-01-01","2003"],"dc:description":["The theoretical results are used to extend the existing learning algorithms. Based on the results from probabilistic classifiers, we have proposed an improved learning algorithm for HMMs which attempts to learn a maximum likelihood classifier under the minimum conditional entropy prior. A margin distribution optimization algorithm is proposed based on the results on generalization bounds and our results show that this new algorithm is better than the existing SVM and boosting algorithms.","Made available in DSpace on 2015-09-25T20:08:17Z (GMT). No. of bitstreams: 2 license.txt: 4848 bytes, checksum: 96035ab3f5e1c23cc7138a224ce498bd (MD5) 3086006.pdf: 6020190 bytes, checksum: 67c25f26bdc4812528c857bef059d8ae (MD5) Previous issue date: 2003","Embargo set by: Seth Robbins for item 82096 Lift date: Forever Reason: Restricted to the U of I community idenfinitely during batch ingest of legacy ETDs","Restricted to the U of I community idenfinitely during batch ingest of legacy ETDs","U of I Only","110 p.","Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2003."],"dc:identifier":["http://hdl.handle.net/2142/80814","(MiAaPQ)AAI3086006"],"dc:language":["eng"],"dc:subject":["Artificial Intelligence"],"dc:title":["Learning in High Dimensional Spaces: Applications, Theory, and Algorithms"],"dc:type":["text"],"thesis:degree_discipline":["Electrical Engineering"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Ph.D."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:26:15Z"}