{"id":"https://openalex.org/W2954938801","doi":"https://doi.org/10.1109/ijcnn.2019.8851839","title":"A Semi-Supervised Self-Organizing Map with Adaptive Local Thresholds","display_name":"A Semi-Supervised Self-Organizing Map with Adaptive Local Thresholds","publication_year":2019,"publication_date":"2019-07-01","ids":{"openalex":"https://openalex.org/W2954938801","doi":"https://doi.org/10.1109/ijcnn.2019.8851839","mag":"2954938801"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn.2019.8851839","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2019.8851839","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"},"type":"article","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1907.01086","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Pedro H. M. Braga","orcid":null},"institutions":[{"id":"https://openalex.org/I25112270","display_name":"Universidade Federal de Pernambuco","ror":"https://ror.org/047908t24","country_code":"BR","type":"education","lineage":["https://openalex.org/I25112270"]}],"countries":["BR"],"is_corresponding":true,"raw_author_name":"Pedro H. M. Braga","raw_affiliation_strings":["Center of Informatics - CIn, Universidade Federal de Pernambuco, Recife, PE, Brazil"],"affiliations":[{"raw_affiliation_string":"Center of Informatics - CIn, Universidade Federal de Pernambuco, Recife, PE, Brazil","institution_ids":["https://openalex.org/I25112270"]}]},{"author_position":"last","author":{"id":null,"display_name":"Hansenclever F. Bassani","orcid":null},"institutions":[{"id":"https://openalex.org/I25112270","display_name":"Universidade Federal de Pernambuco","ror":"https://ror.org/047908t24","country_code":"BR","type":"education","lineage":["https://openalex.org/I25112270"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Hansenclever F. Bassani","raw_affiliation_strings":["Center of Informatics - CIn, Universidade Federal de Pernambuco, Recife, PE, Brazil"],"affiliations":[{"raw_affiliation_string":"Center of Informatics - CIn, Universidade Federal de Pernambuco, Recife, PE, Brazil","institution_ids":["https://openalex.org/I25112270"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I25112270"],"apc_list":null,"apc_paid":null,"fwci":0.1022,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.42660659,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"521","issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.9962999820709229,"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":0.9962999820709229,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9919000267982483,"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/T13717","display_name":"Advanced Algorithms and Applications","score":0.9912999868392944,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"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/cluster-analysis","display_name":"Cluster analysis","score":0.7203999757766724},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.40369999408721924},{"id":"https://openalex.org/keywords/variance","display_name":"Variance (accounting)","score":0.4023999869823456},{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.33889999985694885},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.32429999113082886},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.31630000472068787},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.31540000438690186}],"concepts":[{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.7203999757766724},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6345000267028809},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5967000126838684},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.40369999408721924},{"id":"https://openalex.org/C196083921","wikidata":"https://www.wikidata.org/wiki/Q7915758","display_name":"Variance (accounting)","level":2,"score":0.4023999869823456},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.38659998774528503},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.33889999985694885},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.32429999113082886},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3188000023365021},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.31630000472068787},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.31540000438690186},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.29789999127388},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.2955999970436096},{"id":"https://openalex.org/C125014702","wikidata":"https://www.wikidata.org/wiki/Q4680749","display_name":"Adaptive learning","level":2,"score":0.29429998993873596},{"id":"https://openalex.org/C17212007","wikidata":"https://www.wikidata.org/wiki/Q5511111","display_name":"Fuzzy clustering","level":3,"score":0.2906999886035919},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.2784000039100647},{"id":"https://openalex.org/C77967617","wikidata":"https://www.wikidata.org/wiki/Q4677561","display_name":"Active learning (machine learning)","level":2,"score":0.2678999900817871},{"id":"https://openalex.org/C112972136","wikidata":"https://www.wikidata.org/wiki/Q7595718","display_name":"Stability (learning theory)","level":2,"score":0.26759999990463257},{"id":"https://openalex.org/C207968372","wikidata":"https://www.wikidata.org/wiki/Q310401","display_name":"k-means clustering","level":3,"score":0.2614000141620636},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.25099998712539673}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/ijcnn.2019.8851839","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2019.8851839","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:1907.01086","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1907.01086","pdf_url":"https://arxiv.org/pdf/1907.01086","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:1907.01086","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1907.01086","pdf_url":"https://arxiv.org/pdf/1907.01086","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":27,"referenced_works":["https://openalex.org/W335453456","https://openalex.org/W1630959083","https://openalex.org/W1660104161","https://openalex.org/W1983320747","https://openalex.org/W1990517717","https://openalex.org/W2010053796","https://openalex.org/W2011430131","https://openalex.org/W2044074081","https://openalex.org/W2073852340","https://openalex.org/W2079361215","https://openalex.org/W2115680416","https://openalex.org/W2117355841","https://openalex.org/W2118377301","https://openalex.org/W2158686304","https://openalex.org/W2464720666","https://openalex.org/W2599483899","https://openalex.org/W2896119449","https://openalex.org/W2902356949","https://openalex.org/W2902605166","https://openalex.org/W2919115771","https://openalex.org/W4234409091","https://openalex.org/W4254311734","https://openalex.org/W6623329352","https://openalex.org/W6631190155","https://openalex.org/W6636686307","https://openalex.org/W6675528751","https://openalex.org/W6682494755"],"related_works":[],"abstract_inverted_index":{"In":[0],"the":[1,42,64,68,109,123,135,138,167],"recent":[2],"years,":[3],"there":[4,17,154],"is":[5,18,47,61],"a":[6,19,58,75],"growing":[7],"interest":[8],"in":[9,13,57,114,127,144],"semi-supervised":[10,142],"learning,":[11],"since,":[12],"many":[14],"learning":[15,31,103],"tasks,":[16],"plentiful":[20],"supply":[21],"of":[22,38,67,102,111,140,146],"unlabeled":[23],"data,":[24],"but":[25],"insufficient":[26],"labeled":[27],"ones.":[28],"Hence,":[29],"Semi-Supervised":[30,91],"models":[32],"can":[33,96,118],"benefit":[34],"from":[35],"both":[36],"types":[37],"data":[39,69,129],"to":[40,49,55,63,108,122,166],"improve":[41],"obtained":[43],"performance.":[44],"Also,":[45],"it":[46],"important":[48],"develop":[50],"methods":[51,143,152,165],"that":[52,60,134],"are":[53,155],"easy":[54],"parameterize":[56],"way":[59],"robust":[62],"different":[65],"characteristics":[66],"at":[70,104],"hand.":[71],"This":[72],"article":[73],"presents":[74],"new":[76],"method":[77],"based":[78],"on":[79],"Self-Organizing":[80,92],"Map":[81,93],"(SOM)":[82],"for":[83],"clustering":[84,151],"and":[85,117,148],"classification,":[86,147],"called":[87],"Adaptive":[88],"Local":[89],"Thresholds":[90],"(ALTSS-SOM).":[94],"It":[95],"dynamically":[97],"switch":[98],"between":[99],"two":[100],"forms":[101],"training":[105],"time,":[106],"according":[107],"availability":[110],"labels,":[112],"as":[113],"previous":[115,164],"models,":[116],"automatically":[119],"adjust":[120],"itself":[121],"local":[124],"variance":[125],"observed":[126],"each":[128],"cluster.":[130],"The":[131],"results":[132],"show":[133],"ALTSS-SOM":[136],"surpass":[137],"performance":[139],"other":[141,149],"terms":[145],"pure":[150],"when":[153],"no":[156],"labels":[157],"available,":[158],"being":[159],"also":[160],"less":[161],"sensitive":[162],"than":[163],"parameters":[168],"values.":[169]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2020,"cited_by_count":1}],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2019-07-12T00:00:00"}