{"id":"https://openalex.org/W1992385924","doi":"https://doi.org/10.1109/smc.2014.6974510","title":"A novel classification method with unlearned-class detection based on a gaussian mixture model","display_name":"A novel classification method with unlearned-class detection based on a gaussian mixture model","publication_year":2014,"publication_date":"2014-10-01","ids":{"openalex":"https://openalex.org/W1992385924","doi":"https://doi.org/10.1109/smc.2014.6974510","mag":"1992385924"},"language":"en","primary_location":{"id":"doi:10.1109/smc.2014.6974510","is_oa":false,"landing_page_url":"https://doi.org/10.1109/smc.2014.6974510","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2014 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","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/A5062105133","display_name":"Keisuke Shima","orcid":"https://orcid.org/0000-0002-6206-8663"},"institutions":[{"id":"https://openalex.org/I180203408","display_name":"Yokohama National University","ror":"https://ror.org/03zyp6p76","country_code":"JP","type":"education","lineage":["https://openalex.org/I180203408"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Keisuke Shima","raw_affiliation_strings":["Faculty of Engineering, Yokohama National University 79\u20135 Tokiwadai, Hodogaya-ku, Yokohama, Japan","Faculty of Engineering, Yokohama National University, 79-5 Tokiwadai, Hodogaya-ku, 240-8501 Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Faculty of Engineering, Yokohama National University 79\u20135 Tokiwadai, Hodogaya-ku, Yokohama, Japan","institution_ids":["https://openalex.org/I180203408"]},{"raw_affiliation_string":"Faculty of Engineering, Yokohama National University, 79-5 Tokiwadai, Hodogaya-ku, 240-8501 Japan","institution_ids":["https://openalex.org/I180203408"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100702815","display_name":"Takahiro Aoki","orcid":"https://orcid.org/0009-0002-4451-599X"},"institutions":[{"id":"https://openalex.org/I180203408","display_name":"Yokohama National University","ror":"https://ror.org/03zyp6p76","country_code":"JP","type":"education","lineage":["https://openalex.org/I180203408"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Takahiro Aoki","raw_affiliation_strings":["Graduate School of Engineering, Yokohama National University 79\u20135 Tokiwadai, Hodogaya-ku, Yokohama, Japan","[Graduate School of Engineering, Yokohama National University, 79-5 Tokiwadai, Hodogaya-ku, 240-8501, Japan]"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Graduate School of Engineering, Yokohama National University 79\u20135 Tokiwadai, Hodogaya-ku, Yokohama, Japan","institution_ids":["https://openalex.org/I180203408"]},{"raw_affiliation_string":"[Graduate School of Engineering, Yokohama National University, 79-5 Tokiwadai, Hodogaya-ku, 240-8501, Japan]","institution_ids":["https://openalex.org/I180203408"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.5771,"has_fulltext":false,"cited_by_count":10,"citation_normalized_percentile":{"value":0.67994911,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":96},"biblio":{"volume":"5","issue":null,"first_page":"3726","last_page":"3731"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11667","display_name":"Advanced Chemical Sensor Technologies","score":0.9887999892234802,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11667","display_name":"Advanced Chemical Sensor Technologies","score":0.9887999892234802,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10784","display_name":"Muscle activation and electromyography studies","score":0.9810000061988831,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10429","display_name":"EEG and Brain-Computer Interfaces","score":0.9753000140190125,"subfield":{"id":"https://openalex.org/subfields/2805","display_name":"Cognitive Neuroscience"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/mixture-model","display_name":"Mixture model","score":0.727221667766571},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.7199532985687256},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6887372732162476},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6877248287200928},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.6856997609138489},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.6624591946601868},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.577775239944458},{"id":"https://openalex.org/keywords/a-priori-and-a-posteriori","display_name":"A priori and a posteriori","score":0.4961739480495453},{"id":"https://openalex.org/keywords/maximum-a-posteriori-estimation","display_name":"Maximum a posteriori estimation","score":0.41933467984199524},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.412951797246933},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.35299402475357056},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.33937591314315796},{"id":"https://openalex.org/keywords/maximum-likelihood","display_name":"Maximum likelihood","score":0.2440296709537506},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.19678732752799988},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.11438128352165222}],"concepts":[{"id":"https://openalex.org/C61224824","wikidata":"https://www.wikidata.org/wiki/Q2260434","display_name":"Mixture model","level":2,"score":0.727221667766571},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.7199532985687256},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6887372732162476},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6877248287200928},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.6856997609138489},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.6624591946601868},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.577775239944458},{"id":"https://openalex.org/C75553542","wikidata":"https://www.wikidata.org/wiki/Q178161","display_name":"A priori and a posteriori","level":2,"score":0.4961739480495453},{"id":"https://openalex.org/C9810830","wikidata":"https://www.wikidata.org/wiki/Q635384","display_name":"Maximum a posteriori estimation","level":3,"score":0.41933467984199524},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.412951797246933},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.35299402475357056},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.33937591314315796},{"id":"https://openalex.org/C49781872","wikidata":"https://www.wikidata.org/wiki/Q1045555","display_name":"Maximum likelihood","level":2,"score":0.2440296709537506},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.19678732752799988},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.11438128352165222},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/smc.2014.6974510","is_oa":false,"landing_page_url":"https://doi.org/10.1109/smc.2014.6974510","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2014 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Climate action","score":0.6200000047683716,"id":"https://metadata.un.org/sdg/13"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":15,"referenced_works":["https://openalex.org/W42722137","https://openalex.org/W259786538","https://openalex.org/W1545922961","https://openalex.org/W2048553957","https://openalex.org/W2056706432","https://openalex.org/W2100190024","https://openalex.org/W2108728387","https://openalex.org/W2164006146","https://openalex.org/W2289837065","https://openalex.org/W2305887293","https://openalex.org/W2766736793","https://openalex.org/W2800394774","https://openalex.org/W6609622965","https://openalex.org/W6682610290","https://openalex.org/W6696747327"],"related_works":["https://openalex.org/W4388311650","https://openalex.org/W5922282","https://openalex.org/W1974056099","https://openalex.org/W4245343541","https://openalex.org/W2386077341","https://openalex.org/W2150865841","https://openalex.org/W2138865713","https://openalex.org/W1578916557","https://openalex.org/W2169353922","https://openalex.org/W2100805585"],"abstract_inverted_index":{"This":[0,54],"paper":[1],"proposes":[2],"a":[3,17,32,36,95],"novel":[4,33],"method":[5,55,106,152],"of":[6,25,47,94,103,124],"estimating":[7],"posteriori":[8],"probability":[9],"for":[10,59,107,122],"learned":[11,26,108,125],"and":[12,27,70,85,109,132,147],"unlearned":[13,28,110,133],"classes":[14,29,140],"based":[15,141],"on":[16,142],"Gaussian":[18,83],"mixture":[19],"model":[20],"(GMM).":[21],"With":[22],"prior":[23],"distributions":[24,84],"defined":[30],"as":[31,67,159],"GMM":[34],"incorporating":[35],"one-versus-the-rest":[37],"classifier,":[38],"any":[39],"defined/undefined":[40],"class":[41,111],"can":[42,56],"be":[43,57],"classified":[44,98],"through":[45],"training":[46,52],"the":[48,75,91,101,104,117,150],"classifier":[49],"using":[50],"given":[51],"samples.":[53],"used":[58],"bioelectric":[60],"signal":[61],"discrimination":[62],"in":[63],"various":[64],"applications":[65],"such":[66,158],"human-machine":[68,156],"interfaces":[69,157],"diagnosis":[71],"support":[72],"systems.":[73,163],"In":[74],"experiments":[76],"reported":[77],"here,":[78],"artificial":[79],"data":[80],"generated":[81],"from":[82,90],"electromyogram":[86],"(EMG)":[87],"patterns":[88],"measured":[89],"forearm":[92],"muscles":[93],"volunteer":[96],"were":[97],"to":[99,155],"demonstrate":[100],"capabilities":[102],"proposed":[105,151],"discrimination.":[112],"The":[113],"results":[114],"showed":[115],"that":[116,149],"approach":[118],"produces":[119],"high":[120],"performance":[121],"classification":[123],"(artificial":[126,134],"data:":[127,135],"100%;":[128],"EMG":[129,137],"patterns:":[130,138],"95.6%)":[131],"93.4%;":[136],"70.4%)":[139],"simple":[143],"neural":[144],"network":[145],"comparison,":[146],"indicated":[148],"is":[153],"applicable":[154],"prosthetic":[160],"hand":[161],"control":[162]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":2},{"year":2018,"cited_by_count":1},{"year":2017,"cited_by_count":1},{"year":2016,"cited_by_count":2}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
