{"id":"https://openalex.org/W3004251094","doi":"https://doi.org/10.1109/indin41052.2019.8972184","title":"Piecewise Large Margin Learning for Partially Annotated Sequences","display_name":"Piecewise Large Margin Learning for Partially Annotated Sequences","publication_year":2019,"publication_date":"2019-07-01","ids":{"openalex":"https://openalex.org/W3004251094","doi":"https://doi.org/10.1109/indin41052.2019.8972184","mag":"3004251094"},"language":"en","primary_location":{"id":"doi:10.1109/indin41052.2019.8972184","is_oa":false,"landing_page_url":"https://doi.org/10.1109/indin41052.2019.8972184","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE 17th International Conference on Industrial Informatics (INDIN)","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/A5048386675","display_name":"Xiaolei Lu","orcid":"https://orcid.org/0000-0002-0953-9219"},"institutions":[{"id":"https://openalex.org/I168719708","display_name":"City University of Hong Kong","ror":"https://ror.org/03q8dnn23","country_code":"HK","type":"education","lineage":["https://openalex.org/I168719708"]}],"countries":["HK"],"is_corresponding":true,"raw_author_name":"Xiaolei LU","raw_affiliation_strings":["City University of Hong Kong,Department of Electronic Engineering","Department of Electronic Engineering, City University of Hong Kong"],"affiliations":[{"raw_affiliation_string":"City University of Hong Kong,Department of Electronic Engineering","institution_ids":["https://openalex.org/I168719708"]},{"raw_affiliation_string":"Department of Electronic Engineering, City University of Hong Kong","institution_ids":["https://openalex.org/I168719708"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5050716572","display_name":"Tommy W. S. Chow","orcid":"https://orcid.org/0000-0001-7051-0434"},"institutions":[{"id":"https://openalex.org/I168719708","display_name":"City University of Hong Kong","ror":"https://ror.org/03q8dnn23","country_code":"HK","type":"education","lineage":["https://openalex.org/I168719708"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Tommy W. S. CHOW","raw_affiliation_strings":["City University of Hong Kong,Department of Electronic Engineering","Department of Electronic Engineering, City University of Hong Kong"],"affiliations":[{"raw_affiliation_string":"City University of Hong Kong,Department of Electronic Engineering","institution_ids":["https://openalex.org/I168719708"]},{"raw_affiliation_string":"Department of Electronic Engineering, City University of Hong Kong","institution_ids":["https://openalex.org/I168719708"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100458457","display_name":"Haijun Zhang","orcid":"https://orcid.org/0000-0002-1648-0227"},"institutions":[{"id":"https://openalex.org/I204983213","display_name":"Harbin Institute of Technology","ror":"https://ror.org/01yqg2h08","country_code":"CN","type":"education","lineage":["https://openalex.org/I204983213"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Haijun ZHANG","raw_affiliation_strings":["Harbin Institute of Technology,Shenzhen Graduate School","Shenzhen Graduate School, Harbin Institute of Technology"],"affiliations":[{"raw_affiliation_string":"Harbin Institute of Technology,Shenzhen Graduate School","institution_ids":["https://openalex.org/I204983213"]},{"raw_affiliation_string":"Shenzhen Graduate School, Harbin Institute of Technology","institution_ids":["https://openalex.org/I204983213"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5048386675"],"corresponding_institution_ids":["https://openalex.org/I168719708"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.17785688,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1483","last_page":"1487"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11309","display_name":"Music and Audio Processing","score":0.9934999942779541,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T11309","display_name":"Music and Audio Processing","score":0.9934999942779541,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T11439","display_name":"Video Analysis and Summarization","score":0.9912999868392944,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T10812","display_name":"Human Pose and Action Recognition","score":0.9861000180244446,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/margin","display_name":"Margin (machine learning)","score":0.8144263029098511},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7586250305175781},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.7040563821792603},{"id":"https://openalex.org/keywords/sequence","display_name":"Sequence (biology)","score":0.6712710857391357},{"id":"https://openalex.org/keywords/sequence-labeling","display_name":"Sequence labeling","score":0.6539192199707031},{"id":"https://openalex.org/keywords/piecewise","display_name":"Piecewise","score":0.6099523901939392},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6024702787399292},{"id":"https://openalex.org/keywords/dictionary-learning","display_name":"Dictionary learning","score":0.5805466771125793},{"id":"https://openalex.org/keywords/structured-prediction","display_name":"Structured prediction","score":0.5371779799461365},{"id":"https://openalex.org/keywords/semi-supervised-learning","display_name":"Semi-supervised learning","score":0.4877239167690277},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4472358524799347},{"id":"https://openalex.org/keywords/regular-polygon","display_name":"Regular polygon","score":0.4454953670501709},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.36114031076431274},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.16264766454696655},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.07031390070915222},{"id":"https://openalex.org/keywords/biology","display_name":"Biology","score":0.06213599443435669}],"concepts":[{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.8144263029098511},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7586250305175781},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.7040563821792603},{"id":"https://openalex.org/C2778112365","wikidata":"https://www.wikidata.org/wiki/Q3511065","display_name":"Sequence (biology)","level":2,"score":0.6712710857391357},{"id":"https://openalex.org/C35639132","wikidata":"https://www.wikidata.org/wiki/Q7452468","display_name":"Sequence labeling","level":3,"score":0.6539192199707031},{"id":"https://openalex.org/C164660894","wikidata":"https://www.wikidata.org/wiki/Q2037833","display_name":"Piecewise","level":2,"score":0.6099523901939392},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6024702787399292},{"id":"https://openalex.org/C2988886741","wikidata":"https://www.wikidata.org/wiki/Q25304494","display_name":"Dictionary learning","level":3,"score":0.5805466771125793},{"id":"https://openalex.org/C22367795","wikidata":"https://www.wikidata.org/wiki/Q7625208","display_name":"Structured prediction","level":2,"score":0.5371779799461365},{"id":"https://openalex.org/C58973888","wikidata":"https://www.wikidata.org/wiki/Q1041418","display_name":"Semi-supervised learning","level":2,"score":0.4877239167690277},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4472358524799347},{"id":"https://openalex.org/C112680207","wikidata":"https://www.wikidata.org/wiki/Q714886","display_name":"Regular polygon","level":2,"score":0.4454953670501709},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.36114031076431274},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.16264766454696655},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.07031390070915222},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.06213599443435669},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.0},{"id":"https://openalex.org/C54355233","wikidata":"https://www.wikidata.org/wiki/Q7162","display_name":"Genetics","level":1,"score":0.0},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"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":1,"locations":[{"id":"doi:10.1109/indin41052.2019.8972184","is_oa":false,"landing_page_url":"https://doi.org/10.1109/indin41052.2019.8972184","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE 17th International Conference on Industrial Informatics (INDIN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","score":0.7300000190734863,"display_name":"Reduced inequalities"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":26,"referenced_works":["https://openalex.org/W1857926807","https://openalex.org/W1964965834","https://openalex.org/W2027233318","https://openalex.org/W2096507791","https://openalex.org/W2098921539","https://openalex.org/W2107108030","https://openalex.org/W2114361266","https://openalex.org/W2144372981","https://openalex.org/W2150969560","https://openalex.org/W2153635508","https://openalex.org/W2158188757","https://openalex.org/W2158681777","https://openalex.org/W2161044106","https://openalex.org/W2547884650","https://openalex.org/W2885917254","https://openalex.org/W2951299559","https://openalex.org/W2952336062","https://openalex.org/W2997519153","https://openalex.org/W4293390546","https://openalex.org/W6639073361","https://openalex.org/W6676273574","https://openalex.org/W6681102160","https://openalex.org/W6683226453","https://openalex.org/W6683235360","https://openalex.org/W6729677316","https://openalex.org/W6764685693"],"related_works":["https://openalex.org/W1828907538","https://openalex.org/W4295805486","https://openalex.org/W2783701116","https://openalex.org/W2153315159","https://openalex.org/W2295786646","https://openalex.org/W2019209885","https://openalex.org/W2755555666","https://openalex.org/W3081027881","https://openalex.org/W1270326974","https://openalex.org/W2296362925"],"abstract_inverted_index":{"Supervised":[0],"and":[1,37],"semi-supervised":[2],"sequence":[3,61],"labeling":[4,62],"methods":[5],"require":[6],"large":[7],"amounts":[8],"of":[9,17,22,75,86,99,106],"fully":[10],"annotated":[11,26,70],"training":[12,65],"sequences":[13,27],"or":[14],"exact":[15],"annotations":[16],"structured":[18,57],"outputs.":[19],"The":[20],"problem":[21],"learning":[23,58],"from":[24,48],"partially":[25,69],"arises":[28],"in":[29,103],"many":[30],"applications,":[31],"for":[32,60,68,79],"example,":[33],"Natural":[34],"Language":[35],"Processing":[36],"Computational":[38],"Biology.":[39],"In":[40],"this":[41],"paper,":[42],"we":[43],"propose":[44],"Piecewise":[45],"Convex":[46],"Learning":[47],"Partial":[49],"Labels":[50],"(PW-CLPL)":[51],"which":[52,82],"is":[53,66,77],"an":[54],"effective":[55],"discriminative":[56],"method":[59],"as":[63],"global":[64],"intractable":[67],"sequences.":[71],"A":[72],"small":[73],"number":[74],"constraints":[76],"reformulated":[78],"the":[80,84,92,97,100,104],"optimization,":[81],"improve":[83],"efficiency":[85],"parameters":[87],"learning.":[88],"Experimental":[89],"results":[90],"on":[91],"reconstructed":[93],"dataset":[94],"CoNLL-2000":[95],"show":[96],"effectiveness":[98],"proposed":[101],"model":[102],"setting":[105],"partial":[107],"annotations.":[108]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}