{"id":"https://openalex.org/W2495842449","doi":"https://doi.org/10.1080/03610918.2016.1189568","title":"A robust support vector machine for labeling errors","display_name":"A robust support vector machine for labeling errors","publication_year":2016,"publication_date":"2016-05-29","ids":{"openalex":"https://openalex.org/W2495842449","doi":"https://doi.org/10.1080/03610918.2016.1189568","mag":"2495842449"},"language":"en","primary_location":{"id":"doi:10.1080/03610918.2016.1189568","is_oa":false,"landing_page_url":"https://doi.org/10.1080/03610918.2016.1189568","pdf_url":null,"source":{"id":"https://openalex.org/S153329750","display_name":"Communications in Statistics - Simulation and Computation","issn_l":"0361-0918","issn":["0361-0918","1532-4141"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Communications in Statistics - Simulation and Computation","raw_type":"journal-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/A5067277192","display_name":"Hosik Choi","orcid":"https://orcid.org/0000-0003-0589-8043"},"institutions":[{"id":"https://openalex.org/I28615091","display_name":"Kyonggi University","ror":"https://ror.org/032xf8h46","country_code":"KR","type":"education","lineage":["https://openalex.org/I28615091"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Hosik Choi","raw_affiliation_strings":["Department of Applied Information Statistics, Kyonggi University, Suwon-si, Gyeonggi-do, South Korea"],"affiliations":[{"raw_affiliation_string":"Department of Applied Information Statistics, Kyonggi University, Suwon-si, Gyeonggi-do, South Korea","institution_ids":["https://openalex.org/I28615091"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5011264910","display_name":"Yongdai Kim","orcid":"https://orcid.org/0000-0002-9434-5645"},"institutions":[{"id":"https://openalex.org/I139264467","display_name":"Seoul National University","ror":"https://ror.org/04h9pn542","country_code":"KR","type":"education","lineage":["https://openalex.org/I139264467"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Yongdai Kim","raw_affiliation_strings":["Department of Statistics, Seoul National University, Gwanak-gu, Seoul, South Korea"],"affiliations":[{"raw_affiliation_string":"Department of Statistics, Seoul National University, Gwanak-gu, Seoul, South Korea","institution_ids":["https://openalex.org/I139264467"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5029708914","display_name":"Sunghoon Kwon","orcid":"https://orcid.org/0000-0003-2722-375X"},"institutions":[{"id":"https://openalex.org/I24062138","display_name":"Konkuk University","ror":"https://ror.org/025h1m602","country_code":"KR","type":"education","lineage":["https://openalex.org/I24062138"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Sunghoon Kwon","raw_affiliation_strings":["Department of Applied Statistics, Konkuk University, Gwangjin-gu, Seoul, South Korea"],"affiliations":[{"raw_affiliation_string":"Department of Applied Statistics, Konkuk University, Gwangjin-gu, Seoul, South Korea","institution_ids":["https://openalex.org/I24062138"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5074268630","display_name":"Changyi Park","orcid":"https://orcid.org/0000-0002-5210-4739"},"institutions":[{"id":"https://openalex.org/I124633538","display_name":"University of Seoul","ror":"https://ror.org/05en5nh73","country_code":"KR","type":"education","lineage":["https://openalex.org/I124633538"]}],"countries":["KR"],"is_corresponding":true,"raw_author_name":"Changyi Park","raw_affiliation_strings":["Department of Statistics, University of Seoul, Seoul, South Korea"],"affiliations":[{"raw_affiliation_string":"Department of Statistics, University of Seoul, Seoul, South Korea","institution_ids":["https://openalex.org/I124633538"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5074268630"],"corresponding_institution_ids":["https://openalex.org/I124633538"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.07822673,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"46","issue":"8","first_page":"6061","last_page":"6073"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11871","display_name":"Advanced Statistical Methods and Models","score":0.9894999861717224,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11871","display_name":"Advanced Statistical Methods and Models","score":0.9894999861717224,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.9891999959945679,"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/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9799000024795532,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.878894567489624},{"id":"https://openalex.org/keywords/outlier","display_name":"Outlier","score":0.8281267285346985},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.607866644859314},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6077200174331665},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.598469614982605},{"id":"https://openalex.org/keywords/hinge-loss","display_name":"Hinge loss","score":0.5829998254776001},{"id":"https://openalex.org/keywords/convexity","display_name":"Convexity","score":0.5788697004318237},{"id":"https://openalex.org/keywords/structured-support-vector-machine","display_name":"Structured support vector machine","score":0.571679949760437},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.5671124458312988},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.5441554188728333},{"id":"https://openalex.org/keywords/margin-classifier","display_name":"Margin classifier","score":0.46759361028671265},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.4611736536026001},{"id":"https://openalex.org/keywords/regular-polygon","display_name":"Regular polygon","score":0.44709816575050354},{"id":"https://openalex.org/keywords/relevance-vector-machine","display_name":"Relevance vector machine","score":0.4212720990180969},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.36953967809677124},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3394986391067505},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.28538304567337036}],"concepts":[{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.878894567489624},{"id":"https://openalex.org/C79337645","wikidata":"https://www.wikidata.org/wiki/Q779824","display_name":"Outlier","level":2,"score":0.8281267285346985},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.607866644859314},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6077200174331665},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.598469614982605},{"id":"https://openalex.org/C39891107","wikidata":"https://www.wikidata.org/wiki/Q5767098","display_name":"Hinge loss","level":3,"score":0.5829998254776001},{"id":"https://openalex.org/C72134830","wikidata":"https://www.wikidata.org/wiki/Q5166524","display_name":"Convexity","level":2,"score":0.5788697004318237},{"id":"https://openalex.org/C125168437","wikidata":"https://www.wikidata.org/wiki/Q7625184","display_name":"Structured support vector machine","level":3,"score":0.571679949760437},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5671124458312988},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.5441554188728333},{"id":"https://openalex.org/C173102733","wikidata":"https://www.wikidata.org/wiki/Q6760396","display_name":"Margin classifier","level":3,"score":0.46759361028671265},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.4611736536026001},{"id":"https://openalex.org/C112680207","wikidata":"https://www.wikidata.org/wiki/Q714886","display_name":"Regular polygon","level":2,"score":0.44709816575050354},{"id":"https://openalex.org/C14948415","wikidata":"https://www.wikidata.org/wiki/Q7310972","display_name":"Relevance vector machine","level":3,"score":0.4212720990180969},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.36953967809677124},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3394986391067505},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.28538304567337036},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C106159729","wikidata":"https://www.wikidata.org/wiki/Q2294553","display_name":"Financial economics","level":1,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1080/03610918.2016.1189568","is_oa":false,"landing_page_url":"https://doi.org/10.1080/03610918.2016.1189568","pdf_url":null,"source":{"id":"https://openalex.org/S153329750","display_name":"Communications in Statistics - Simulation and Computation","issn_l":"0361-0918","issn":["0361-0918","1532-4141"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Communications in Statistics - Simulation and Computation","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/16","display_name":"Peace, Justice and strong institutions","score":0.7799999713897705}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W149581298","https://openalex.org/W1480222970","https://openalex.org/W1969515697","https://openalex.org/W1976564639","https://openalex.org/W2007527993","https://openalex.org/W2027688434","https://openalex.org/W2030843733","https://openalex.org/W2087684630","https://openalex.org/W2129249398","https://openalex.org/W2130996889","https://openalex.org/W2133958955","https://openalex.org/W2134389439","https://openalex.org/W2138449831","https://openalex.org/W2141718589","https://openalex.org/W2145147745","https://openalex.org/W2153417333","https://openalex.org/W2159400887","https://openalex.org/W2187013920","https://openalex.org/W2546448115"],"related_works":["https://openalex.org/W2388218200","https://openalex.org/W2384390720","https://openalex.org/W2122277321","https://openalex.org/W4253128984","https://openalex.org/W2539940072","https://openalex.org/W2377603574","https://openalex.org/W2461871226","https://openalex.org/W2371356469","https://openalex.org/W2130972437","https://openalex.org/W3048587023"],"abstract_inverted_index":{"Support":[0],"vector":[1],"machine":[2],"(SVM)":[3],"is":[4,10,26,55,60],"sparse":[5],"in":[6,31,94],"that":[7,54,88],"its":[8],"classifier":[9],"expressed":[11],"as":[12,28],"a":[13,18,50],"linear":[14],"combination":[15],"of":[16,70,97],"only":[17],"few":[19],"support":[20],"vectors":[21],"(SVs).":[22],"Whenever":[23],"an":[24,29,75],"outlier":[25,35],"included":[27],"SV":[30],"the":[32,34,41,68,95],"classifier,":[33],"may":[36],"have":[37],"serious":[38],"impact":[39],"on":[40],"estimated":[42],"decision":[43],"function.":[44],"In":[45],"this":[46],"article,":[47],"we":[48,86],"propose":[49],"robust":[51,62],"loss":[52,72],"function":[53,73],"convex.":[56],"Our":[57],"learning":[58],"algorithm":[59],"more":[61],"to":[63],"outliers":[64],"than":[65],"SVM.":[66],"Also":[67],"convexity":[69],"our":[71,89],"permits":[74],"efficient":[76],"solution":[77],"path":[78],"algorithm.":[79],"Through":[80],"simulated":[81],"and":[82],"real":[83],"data":[84],"analysis,":[85],"illustrate":[87],"method":[90],"can":[91],"be":[92],"useful":[93],"presence":[96],"labeling":[98],"errors.":[99]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}