{"id":"https://openalex.org/W2168772685","doi":"https://doi.org/10.1145/1143844.1143977","title":"Discriminative unsupervised learning of structured predictors","display_name":"Discriminative unsupervised learning of structured predictors","publication_year":2006,"publication_date":"2006-01-01","ids":{"openalex":"https://openalex.org/W2168772685","doi":"https://doi.org/10.1145/1143844.1143977","mag":"2168772685"},"language":"en","primary_location":{"id":"doi:10.1145/1143844.1143977","is_oa":false,"landing_page_url":"https://doi.org/10.1145/1143844.1143977","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 23rd international conference on Machine learning  - ICML '06","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5009732907","display_name":"Linli Xu","orcid":"https://orcid.org/0000-0003-0227-3793"},"institutions":[{"id":"https://openalex.org/I151746483","display_name":"University of Waterloo","ror":"https://ror.org/01aff2v68","country_code":"CA","type":"education","lineage":["https://openalex.org/I151746483"]}],"countries":["CA"],"is_corresponding":true,"raw_author_name":"Linli Xu","raw_affiliation_strings":["University of Waterloo, Waterloo ON, Canada"],"affiliations":[{"raw_affiliation_string":"University of Waterloo, Waterloo ON, Canada","institution_ids":["https://openalex.org/I151746483"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5014163352","display_name":"Dana Wilkinson","orcid":null},"institutions":[{"id":"https://openalex.org/I151746483","display_name":"University of Waterloo","ror":"https://ror.org/01aff2v68","country_code":"CA","type":"education","lineage":["https://openalex.org/I151746483"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Dana Wilkinson","raw_affiliation_strings":["University of Waterloo, Waterloo ON, Canada"],"affiliations":[{"raw_affiliation_string":"University of Waterloo, Waterloo ON, Canada","institution_ids":["https://openalex.org/I151746483"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5047278339","display_name":"Finnegan Southey","orcid":null},"institutions":[{"id":"https://openalex.org/I154425047","display_name":"University of Alberta","ror":"https://ror.org/0160cpw27","country_code":"CA","type":"education","lineage":["https://openalex.org/I154425047"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Finnegan Southey","raw_affiliation_strings":["University of Alberta, Edmonton AB, Canada"],"affiliations":[{"raw_affiliation_string":"University of Alberta, Edmonton AB, Canada","institution_ids":["https://openalex.org/I154425047"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5010575626","display_name":"Dale Schuurmans","orcid":null},"institutions":[{"id":"https://openalex.org/I154425047","display_name":"University of Alberta","ror":"https://ror.org/0160cpw27","country_code":"CA","type":"education","lineage":["https://openalex.org/I154425047"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Dale Schuurmans","raw_affiliation_strings":["University of Alberta, Edmonton AB, Canada"],"affiliations":[{"raw_affiliation_string":"University of Alberta, Edmonton AB, Canada","institution_ids":["https://openalex.org/I154425047"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5009732907"],"corresponding_institution_ids":["https://openalex.org/I151746483"],"apc_list":null,"apc_paid":null,"fwci":8.3737,"has_fulltext":false,"cited_by_count":42,"citation_normalized_percentile":{"value":0.97483681,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1057","last_page":"1064"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11303","display_name":"Bayesian Modeling and Causal Inference","score":0.9991000294685364,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11303","display_name":"Bayesian Modeling and Causal Inference","score":0.9991000294685364,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12072","display_name":"Machine Learning and Algorithms","score":0.9990000128746033,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.9955000281333923,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.8901117444038391},{"id":"https://openalex.org/keywords/semidefinite-programming","display_name":"Semidefinite programming","score":0.6578347086906433},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.643161416053772},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.613365888595581},{"id":"https://openalex.org/keywords/margin","display_name":"Margin (machine learning)","score":0.612162709236145},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6047313213348389},{"id":"https://openalex.org/keywords/maxima-and-minima","display_name":"Maxima and minima","score":0.5974780321121216},{"id":"https://openalex.org/keywords/hidden-markov-model","display_name":"Hidden Markov model","score":0.5227275490760803},{"id":"https://openalex.org/keywords/semidefinite-embedding","display_name":"Semidefinite embedding","score":0.5136982202529907},{"id":"https://openalex.org/keywords/markov-process","display_name":"Markov process","score":0.4903161823749542},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.488724946975708},{"id":"https://openalex.org/keywords/unsupervised-learning","display_name":"Unsupervised learning","score":0.48690927028656006},{"id":"https://openalex.org/keywords/convex-optimization","display_name":"Convex optimization","score":0.4533616006374359},{"id":"https://openalex.org/keywords/heuristic","display_name":"Heuristic","score":0.4400699734687805},{"id":"https://openalex.org/keywords/large-margin-nearest-neighbor","display_name":"Large margin nearest neighbor","score":0.41652336716651917},{"id":"https://openalex.org/keywords/linear-programming","display_name":"Linear programming","score":0.41363513469696045},{"id":"https://openalex.org/keywords/regular-polygon","display_name":"Regular polygon","score":0.3814283609390259},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.33159369230270386},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.23389464616775513},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.17973476648330688},{"id":"https://openalex.org/keywords/k-nearest-neighbors-algorithm","display_name":"k-nearest neighbors algorithm","score":0.10451292991638184},{"id":"https://openalex.org/keywords/quadratically-constrained-quadratic-program","display_name":"Quadratically constrained quadratic program","score":0.10422873497009277},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.08462280035018921}],"concepts":[{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.8901117444038391},{"id":"https://openalex.org/C101901036","wikidata":"https://www.wikidata.org/wiki/Q2269096","display_name":"Semidefinite programming","level":2,"score":0.6578347086906433},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.643161416053772},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.613365888595581},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.612162709236145},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6047313213348389},{"id":"https://openalex.org/C186633575","wikidata":"https://www.wikidata.org/wiki/Q845060","display_name":"Maxima and minima","level":2,"score":0.5974780321121216},{"id":"https://openalex.org/C23224414","wikidata":"https://www.wikidata.org/wiki/Q176769","display_name":"Hidden Markov model","level":2,"score":0.5227275490760803},{"id":"https://openalex.org/C206818286","wikidata":"https://www.wikidata.org/wiki/Q7449403","display_name":"Semidefinite embedding","level":4,"score":0.5136982202529907},{"id":"https://openalex.org/C159886148","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov process","level":2,"score":0.4903161823749542},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.488724946975708},{"id":"https://openalex.org/C8038995","wikidata":"https://www.wikidata.org/wiki/Q1152135","display_name":"Unsupervised learning","level":2,"score":0.48690927028656006},{"id":"https://openalex.org/C157972887","wikidata":"https://www.wikidata.org/wiki/Q463359","display_name":"Convex optimization","level":3,"score":0.4533616006374359},{"id":"https://openalex.org/C173801870","wikidata":"https://www.wikidata.org/wiki/Q201413","display_name":"Heuristic","level":2,"score":0.4400699734687805},{"id":"https://openalex.org/C94475309","wikidata":"https://www.wikidata.org/wiki/Q6489154","display_name":"Large margin nearest neighbor","level":3,"score":0.41652336716651917},{"id":"https://openalex.org/C41045048","wikidata":"https://www.wikidata.org/wiki/Q202843","display_name":"Linear programming","level":2,"score":0.41363513469696045},{"id":"https://openalex.org/C112680207","wikidata":"https://www.wikidata.org/wiki/Q714886","display_name":"Regular polygon","level":2,"score":0.3814283609390259},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.33159369230270386},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.23389464616775513},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.17973476648330688},{"id":"https://openalex.org/C113238511","wikidata":"https://www.wikidata.org/wiki/Q1071612","display_name":"k-nearest neighbors algorithm","level":2,"score":0.10451292991638184},{"id":"https://openalex.org/C65148998","wikidata":"https://www.wikidata.org/wiki/Q7268373","display_name":"Quadratically constrained quadratic program","level":3,"score":0.10422873497009277},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.08462280035018921},{"id":"https://openalex.org/C81845259","wikidata":"https://www.wikidata.org/wiki/Q290117","display_name":"Quadratic programming","level":2,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.1145/1143844.1143977","is_oa":false,"landing_page_url":"https://doi.org/10.1145/1143844.1143977","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 23rd international conference on Machine learning  - ICML '06","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.126.4376","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.126.4376","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://cs.ualberta.ca/~dale/papers/icml06.pdf","raw_type":"text"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.134.8056","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.134.8056","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.cs.uwaterloo.ca/~l5xu/papers/icml06.pdf","raw_type":"text"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.77.9381","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.77.9381","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.icml2006.org/icml_documents/camera-ready/133_Discriminative_Unsup.pdf","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10","score":0.75}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W147273232","https://openalex.org/W158976495","https://openalex.org/W182831726","https://openalex.org/W1508165687","https://openalex.org/W1553621070","https://openalex.org/W1574901103","https://openalex.org/W1579838312","https://openalex.org/W1632601927","https://openalex.org/W2058497129","https://openalex.org/W2095758845","https://openalex.org/W2105644991","https://openalex.org/W2109501666","https://openalex.org/W2120340025","https://openalex.org/W2125838338","https://openalex.org/W2132820034","https://openalex.org/W2139686264","https://openalex.org/W2147880316","https://openalex.org/W2157791002","https://openalex.org/W2296319761","https://openalex.org/W2429914308","https://openalex.org/W3214380909"],"related_works":["https://openalex.org/W2391916929","https://openalex.org/W2362452378","https://openalex.org/W2364528045","https://openalex.org/W2385886188","https://openalex.org/W2544939111","https://openalex.org/W2524928742","https://openalex.org/W2737505353","https://openalex.org/W121484681","https://openalex.org/W2351113357","https://openalex.org/W2348903644"],"abstract_inverted_index":{"We":[0],"present":[1],"a":[2,48,87],"new":[3],"unsupervised":[4,26,61],"algorithm":[5],"for":[6,52],"training":[7,50,70,75],"structured":[8,29,53],"predictors":[9,54],"that":[10,38,59,90,98],"is":[11,22,47],"discriminative,":[12],"convex,":[13],"and":[14,62,84],"avoids":[15,91],"the":[16,74,79,99],"use":[17],"of":[18,28],"EM.":[19],"The":[20,45],"idea":[21],"to":[23,77],"formulate":[24],"an":[25],"version":[27],"learning":[30],"methods,":[31],"such":[32],"as":[33],"maximum":[34],"margin":[35],"Markov":[36,57],"networks,":[37],"can":[39,103],"be":[40],"trained":[41],"via":[42],"semidefinite":[43,82,92],"programming.":[44],"result":[46],"discriminative":[49,101],"criterion":[51],"(like":[55],"hidden":[56],"models)":[58],"remains":[60],"does":[63],"not":[64],"create":[65],"local":[66],"minima.":[67],"To":[68],"reduce":[69],"cost,":[71],"we":[72],"reformulate":[73],"procedure":[76,89,102],"mitigate":[78],"dependence":[80],"on":[81],"programming,":[83],"finally":[85],"propose":[86],"heuristic":[88],"programming":[93],"entirely.":[94],"Experimental":[95],"results":[96],"show":[97],"convex":[100],"produce":[104],"better":[105],"conditional":[106],"models":[107],"than":[108],"conventional":[109],"Baum-Welch":[110],"(EM)":[111],"training.":[112]},"counts_by_year":[{"year":2021,"cited_by_count":1},{"year":2018,"cited_by_count":1},{"year":2017,"cited_by_count":1},{"year":2016,"cited_by_count":3},{"year":2014,"cited_by_count":1},{"year":2013,"cited_by_count":5},{"year":2012,"cited_by_count":6}],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2025-10-10T00:00:00"}