{"id":"https://openalex.org/W2996711436","doi":"https://doi.org/10.1109/tencon.2019.8929299","title":"Gaussian Mixture Reduction Methods Using Support Vector Machine","display_name":"Gaussian Mixture Reduction Methods Using Support Vector Machine","publication_year":2019,"publication_date":"2019-10-01","ids":{"openalex":"https://openalex.org/W2996711436","doi":"https://doi.org/10.1109/tencon.2019.8929299","mag":"2996711436"},"language":"en","primary_location":{"id":"doi:10.1109/tencon.2019.8929299","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tencon.2019.8929299","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"TENCON 2019 - 2019 IEEE Region 10 Conference (TENCON)","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/A5041397543","display_name":"Haruki Kazama","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Haruki Kazama","raw_affiliation_strings":["NEC Corp"],"affiliations":[{"raw_affiliation_string":"NEC Corp","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5108626659","display_name":"Shuji Tsukiyama","orcid":null},"institutions":[{"id":"https://openalex.org/I96679780","display_name":"Chuo University","ror":"https://ror.org/03qvqb743","country_code":"JP","type":"education","lineage":["https://openalex.org/I96679780"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Shuji Tsukiyama","raw_affiliation_strings":["Department of Electrical, Electronic, and Communication Engineering, Chuo University, 1-13-27 Kasuga, Bunkyo, Tokyo"],"affiliations":[{"raw_affiliation_string":"Department of Electrical, Electronic, and Communication Engineering, Chuo University, 1-13-27 Kasuga, Bunkyo, Tokyo","institution_ids":["https://openalex.org/I96679780"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5041397543"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.15328094,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"42","issue":null,"first_page":"411","last_page":"416"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.9952999949455261,"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":0.9952999949455261,"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/T10320","display_name":"Neural Networks and Applications","score":0.9837999939918518,"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/T10640","display_name":"Spectroscopy and Chemometric Analyses","score":0.9818000197410583,"subfield":{"id":"https://openalex.org/subfields/1602","display_name":"Analytical Chemistry"},"field":{"id":"https://openalex.org/fields/16","display_name":"Chemistry"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6723489165306091},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.654901385307312},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.6341230273246765},{"id":"https://openalex.org/keywords/reduction","display_name":"Reduction (mathematics)","score":0.6280871033668518},{"id":"https://openalex.org/keywords/mixture-model","display_name":"Mixture model","score":0.5080301761627197},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4917023777961731},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.4782613515853882},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.4042906165122986},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3115980625152588},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.23333507776260376}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6723489165306091},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.654901385307312},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.6341230273246765},{"id":"https://openalex.org/C111335779","wikidata":"https://www.wikidata.org/wiki/Q3454686","display_name":"Reduction (mathematics)","level":2,"score":0.6280871033668518},{"id":"https://openalex.org/C61224824","wikidata":"https://www.wikidata.org/wiki/Q2260434","display_name":"Mixture model","level":2,"score":0.5080301761627197},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4917023777961731},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.4782613515853882},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.4042906165122986},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3115980625152588},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.23333507776260376},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","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},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tencon.2019.8929299","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tencon.2019.8929299","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"TENCON 2019 - 2019 IEEE Region 10 Conference (TENCON)","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":12,"referenced_works":["https://openalex.org/W1967709031","https://openalex.org/W2028175459","https://openalex.org/W2033763106","https://openalex.org/W2039134037","https://openalex.org/W2108288396","https://openalex.org/W2119821739","https://openalex.org/W2125105520","https://openalex.org/W2558609891","https://openalex.org/W2771200196","https://openalex.org/W3143528639","https://openalex.org/W4239510810","https://openalex.org/W6676085441"],"related_works":["https://openalex.org/W2090763504","https://openalex.org/W1975321310","https://openalex.org/W148178222","https://openalex.org/W2104657898","https://openalex.org/W1952261593","https://openalex.org/W2014494654","https://openalex.org/W1948992892","https://openalex.org/W1886884218","https://openalex.org/W1910826599","https://openalex.org/W1578916557"],"abstract_inverted_index":{"Gaussian":[0,22],"mixture":[1,23],"model":[2,24],"is":[3,83,112,118],"a":[4,84],"useful":[5],"distribution":[6,82,111],"for":[7,53,79,87],"statistical":[8,12,29],"methods":[9,52,99],"such":[10],"as":[11],"static":[13],"timing":[14],"analysis,":[15],"but":[16,126],"the":[17,32,55,89,109],"number":[18,33,56,90],"of":[19,21,34,57,63,91],"components":[20,35,58],"increases":[25],"exponentially":[26],"by":[27,100,114],"repeating":[28],"operations.":[30],"Hence,":[31],"must":[36],"be":[37],"reduced":[38],"to":[39,44,121],"around":[40],"2":[41],"in":[42,69],"order":[43],"repeat":[45],"operations":[46],"efficiently":[47],"and":[48,67,71,104],"effectively.":[49],"Although":[50],"several":[51],"reducing":[54,88],"have":[59],"been":[60],"proposed,":[61],"each":[62],"them":[64],"has":[65],"strength":[66],"weakness":[68],"accuracy":[70],"CPU":[72],"time.":[73],"Therefore,":[74],"selecting":[75],"an":[76,80,123],"appropriate":[77,124],"method":[78],"input":[81,110],"practical":[85],"way":[86],"components.":[92],"In":[93],"this":[94],"paper,":[95],"we":[96],"construct":[97],"selection":[98,133],"using":[101],"support-vector":[102],"machine":[103,129],"evaluates":[105],"their":[106],"performances.":[107],"Since":[108],"represented":[113],"non-linear":[115],"functions,":[116],"it":[117],"not":[119],"easy":[120],"select":[122],"method,":[125],"support":[127],"vector":[128],"achieved":[130],"94.8%":[131],"correct":[132],"rate.":[134]},"counts_by_year":[{"year":2023,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}