{"id":"https://openalex.org/W2735119411","doi":"https://doi.org/10.1109/ijcnn.2017.7966328","title":"A mixture of multiple linear classifiers with sample weight and manifold regularization","display_name":"A mixture of multiple linear classifiers with sample weight and manifold regularization","publication_year":2017,"publication_date":"2017-05-01","ids":{"openalex":"https://openalex.org/W2735119411","doi":"https://doi.org/10.1109/ijcnn.2017.7966328","mag":"2735119411"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn.2017.7966328","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2017.7966328","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5017086786","display_name":"Weite Li","orcid":null},"institutions":[{"id":"https://openalex.org/I150744194","display_name":"Waseda University","ror":"https://ror.org/00ntfnx83","country_code":"JP","type":"education","lineage":["https://openalex.org/I150744194"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Weite Li","raw_affiliation_strings":["Graduate School of Information, Product and Systems, Waseda University, Wakamatsu, Kitakyushu-shi, Fukuoka, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Graduate School of Information, Product and Systems, Waseda University, Wakamatsu, Kitakyushu-shi, Fukuoka, Japan","institution_ids":["https://openalex.org/I150744194"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5067878297","display_name":"Benhui Chen","orcid":null},"institutions":[{"id":"https://openalex.org/I6593398","display_name":"Dali University","ror":"https://ror.org/02y7rck89","country_code":"CN","type":"education","lineage":["https://openalex.org/I6593398"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Benhui Chen","raw_affiliation_strings":["School of Mathematics and Computer Science, Dali University, Dali, Yunnan Province, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Mathematics and Computer Science, Dali University, Dali, Yunnan Province, China","institution_ids":["https://openalex.org/I6593398"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101442102","display_name":"Bo Zhou","orcid":"https://orcid.org/0000-0002-7824-3673"},"institutions":[{"id":"https://openalex.org/I150744194","display_name":"Waseda University","ror":"https://ror.org/00ntfnx83","country_code":"JP","type":"education","lineage":["https://openalex.org/I150744194"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Bo Zhou","raw_affiliation_strings":["Graduate School of Information, Product and Systems, Waseda University, Wakamatsu, Kitakyushu-shi, Fukuoka, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Graduate School of Information, Product and Systems, Waseda University, Wakamatsu, Kitakyushu-shi, Fukuoka, Japan","institution_ids":["https://openalex.org/I150744194"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100326923","display_name":"Jinglu Hu","orcid":"https://orcid.org/0000-0002-5601-7261"},"institutions":[{"id":"https://openalex.org/I150744194","display_name":"Waseda University","ror":"https://ror.org/00ntfnx83","country_code":"JP","type":"education","lineage":["https://openalex.org/I150744194"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Jinglu Hu","raw_affiliation_strings":["Graduate School of Information, Product and Systems, Waseda University, Wakamatsu, Kitakyushu-shi, Fukuoka, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Graduate School of Information, Product and Systems, Waseda University, Wakamatsu, Kitakyushu-shi, Fukuoka, Japan","institution_ids":["https://openalex.org/I150744194"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"3747","last_page":"3752"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.9976999759674072,"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"}},"topics":[{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.9976999759674072,"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/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.9972000122070312,"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/T11550","display_name":"Text and Document Classification Technologies","score":0.9843000173568726,"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/classifier","display_name":"Classifier (UML)","score":0.6361101269721985},{"id":"https://openalex.org/keywords/weighting","display_name":"Weighting","score":0.6202625036239624},{"id":"https://openalex.org/keywords/mixture-model","display_name":"Mixture model","score":0.5601629018783569},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5537338852882385},{"id":"https://openalex.org/keywords/regularization","display_name":"Regularization (linguistics)","score":0.5180378556251526},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.509627103805542},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.49257785081863403},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.48251745104789734},{"id":"https://openalex.org/keywords/labeled-data","display_name":"Labeled data","score":0.4300650954246521},{"id":"https://openalex.org/keywords/linear-classifier","display_name":"Linear classifier","score":0.4261947274208069},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3828018605709076},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.37776169180870056}],"concepts":[{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.6361101269721985},{"id":"https://openalex.org/C183115368","wikidata":"https://www.wikidata.org/wiki/Q856577","display_name":"Weighting","level":2,"score":0.6202625036239624},{"id":"https://openalex.org/C61224824","wikidata":"https://www.wikidata.org/wiki/Q2260434","display_name":"Mixture model","level":2,"score":0.5601629018783569},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5537338852882385},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.5180378556251526},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.509627103805542},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.49257785081863403},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.48251745104789734},{"id":"https://openalex.org/C2776145971","wikidata":"https://www.wikidata.org/wiki/Q30673951","display_name":"Labeled data","level":2,"score":0.4300650954246521},{"id":"https://openalex.org/C139532973","wikidata":"https://www.wikidata.org/wiki/Q2679259","display_name":"Linear classifier","level":3,"score":0.4261947274208069},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3828018605709076},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.37776169180870056},{"id":"https://openalex.org/C126838900","wikidata":"https://www.wikidata.org/wiki/Q77604","display_name":"Radiology","level":1,"score":0.0},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn.2017.7966328","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2017.7966328","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":29,"referenced_works":["https://openalex.org/W1479807131","https://openalex.org/W1560724230","https://openalex.org/W1664825283","https://openalex.org/W1676661688","https://openalex.org/W1962219632","https://openalex.org/W2054678839","https://openalex.org/W2104290444","https://openalex.org/W2118712128","https://openalex.org/W2118978333","https://openalex.org/W2145406111","https://openalex.org/W2153635508","https://openalex.org/W2155440340","https://openalex.org/W2189508540","https://openalex.org/W2386259472","https://openalex.org/W2498119267","https://openalex.org/W2551959147","https://openalex.org/W2552835936","https://openalex.org/W2621615146","https://openalex.org/W2997701990","https://openalex.org/W3018189936","https://openalex.org/W3195149063","https://openalex.org/W4239510810","https://openalex.org/W4255455317","https://openalex.org/W6637179743","https://openalex.org/W6637406579","https://openalex.org/W6675747103","https://openalex.org/W6677758222","https://openalex.org/W6687078384","https://openalex.org/W6738988723"],"related_works":["https://openalex.org/W2180954594","https://openalex.org/W2052835778","https://openalex.org/W2049003611","https://openalex.org/W2127804977","https://openalex.org/W2108418243","https://openalex.org/W164103134","https://openalex.org/W2787352659","https://openalex.org/W2783129721","https://openalex.org/W3130534623","https://openalex.org/W2160952319"],"abstract_inverted_index":{"A":[0],"mixture":[1,56,94,109,158],"of":[2,45,55,81,93,108,144,150,159],"multiple":[3,160],"linear":[4,22,161],"classifiers":[5,83,162],"is":[6],"famous":[7],"for":[8,116,136],"its":[9,31,49],"efficiency":[10],"and":[11,64,120,168],"effectiveness":[12],"to":[13,34,124,166],"tackle":[14],"nonlinear":[15],"classification":[16],"problems.":[17,171],"Each":[18],"classifier":[19,33],"contains":[20],"one":[21],"function":[23],"multiplied":[24],"with":[25,131],"a":[26,35,112,121,155,157],"gated":[27],"function,":[28],"which":[29,73],"restricts":[30],"corresponding":[32],"local":[36,46],"region.":[37],"Previous":[38],"researches":[39],"mainly":[40],"focus":[41],"on":[42,78],"the":[43,53,79,91,105,142,148,151],"partition":[44],"regions,":[47],"since":[48],"quality":[50],"directly":[51],"determines":[52],"performance":[54,80],"models.":[57,95],"However,":[58],"in":[59,90,141],"real-world":[60],"data":[61,67,118],"sets,":[62],"imbalanced":[63,117,167],"insufficient":[65],"labeled":[66],"are":[68,85,102,134],"two":[69,129],"frequently":[70],"encountered":[71],"problems,":[72],"also":[74],"have":[75],"large":[76],"influences":[77],"learned":[82],"but":[84],"seldom":[86],"considered":[87],"or":[88],"explored":[89],"context":[92],"In":[96],"this":[97],"paper,":[98],"these":[99],"missing":[100],"components":[101],"introduced":[103],"into":[104],"original":[106],"formulation":[107],"models,":[110],"namely,":[111],"sample":[113],"weighting":[114],"scheme":[115],"distributions":[119],"manifold":[122],"regularization":[123],"leverage":[125],"unlabeled":[126],"data.":[127],"Then,":[128],"solutions":[130],"closed":[132],"form":[133],"provided":[135],"parameter":[137],"optimization.":[138],"Experimental":[139],"results":[140],"end":[143],"our":[145],"paper":[146],"exhibit":[147],"significance":[149],"added":[152],"components.":[153],"As":[154],"result,":[156],"can":[163],"be":[164],"extended":[165],"semi-supervised":[169],"learning":[170]},"counts_by_year":[{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
