{"id":"https://openalex.org/W1994795982","doi":"https://doi.org/10.4018/japuc.2012100107","title":"A New SVM Reduction Strategy of Large-Scale Training Sample Sets","display_name":"A New SVM Reduction Strategy of Large-Scale Training Sample Sets","publication_year":2012,"publication_date":"2012-10-01","ids":{"openalex":"https://openalex.org/W1994795982","doi":"https://doi.org/10.4018/japuc.2012100107","mag":"1994795982"},"language":"en","primary_location":{"id":"doi:10.4018/japuc.2012100107","is_oa":false,"landing_page_url":"https://doi.org/10.4018/japuc.2012100107","pdf_url":null,"source":{"id":"https://openalex.org/S16161090","display_name":"International Journal of Advanced Pervasive and Ubiquitous Computing","issn_l":"1937-965X","issn":["1937-965X","1937-9668"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320424","host_organization_name":"IGI Global","host_organization_lineage":["https://openalex.org/P4310320424"],"host_organization_lineage_names":["IGI Global"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Advanced Pervasive and Ubiquitous Computing","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/A5042675028","display_name":"Fang Zhu","orcid":"https://orcid.org/0000-0003-4254-819X"},"institutions":[{"id":"https://openalex.org/I9224756","display_name":"Northeastern University","ror":"https://ror.org/03awzbc87","country_code":"CN","type":"education","lineage":["https://openalex.org/I9224756"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Fang Zhu","raw_affiliation_strings":["School of Computer and Communication Engineering, Northeastern University, Qinhuangdao, China","[School of Computer and Communication Engineering, Northeastern University, Qinhuangdao, China]"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer and Communication Engineering, Northeastern University, Qinhuangdao, China","institution_ids":["https://openalex.org/I9224756"]},{"raw_affiliation_string":"[School of Computer and Communication Engineering, Northeastern University, Qinhuangdao, China]","institution_ids":["https://openalex.org/I9224756"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5001659885","display_name":"Junfang Wei","orcid":"https://orcid.org/0000-0001-6512-6585"},"institutions":[{"id":"https://openalex.org/I201532990","display_name":"Tianjin Foreign Studies University","ror":"https://ror.org/057hdmq26","country_code":"CN","type":"education","lineage":["https://openalex.org/I201532990"]},{"id":"https://openalex.org/I9224756","display_name":"Northeastern University","ror":"https://ror.org/03awzbc87","country_code":"CN","type":"education","lineage":["https://openalex.org/I9224756"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Junfang Wei","raw_affiliation_strings":["School of Resource and Material, Northeastern University, Qinhuangdao, China & Tianjin Foreign Studies University, TianJin, China","School of Resource and Material, Northeastern University, Qinhuangdao, China & Tianjin Foreign Studies University, TianJin, China#TAB#"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Resource and Material, Northeastern University, Qinhuangdao, China & Tianjin Foreign Studies University, TianJin, China","institution_ids":["https://openalex.org/I201532990","https://openalex.org/I9224756"]},{"raw_affiliation_string":"School of Resource and Material, Northeastern University, Qinhuangdao, China & Tianjin Foreign Studies University, TianJin, China#TAB#","institution_ids":["https://openalex.org/I201532990"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100738070","display_name":"Tao Gao","orcid":"https://orcid.org/0000-0002-6998-7654"},"institutions":[{"id":"https://openalex.org/I153473198","display_name":"North China Electric Power University","ror":"https://ror.org/04qr5t414","country_code":"CN","type":"education","lineage":["https://openalex.org/I153473198"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tao Gao","raw_affiliation_strings":["North China Electric Power University, Beijing, China & Electronic Information Products Supervision and Inspection Institute of Hebei Province, ShijiaZhuang, China"],"raw_orcid":"https://orcid.org/0000-0002-6998-7654","affiliations":[{"raw_affiliation_string":"North China Electric Power University, Beijing, China & Electronic Information Products Supervision and Inspection Institute of Hebei Province, ShijiaZhuang, China","institution_ids":["https://openalex.org/I153473198"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.07525114,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"4","issue":"4","first_page":"63","last_page":"73"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.9962000250816345,"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":0.9962000250816345,"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/T13890","display_name":"Remote Sensing and Land Use","score":0.9785000085830688,"subfield":{"id":"https://openalex.org/subfields/1902","display_name":"Atmospheric Science"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T13717","display_name":"Advanced Algorithms and Applications","score":0.97079998254776,"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/support-vector-machine","display_name":"Support vector machine","score":0.7636299133300781},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.687751829624176},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.6379482746124268},{"id":"https://openalex.org/keywords/outlier","display_name":"Outlier","score":0.6118946075439453},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5935126543045044},{"id":"https://openalex.org/keywords/reduction","display_name":"Reduction (mathematics)","score":0.5798361301422119},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.5612143278121948},{"id":"https://openalex.org/keywords/bottleneck","display_name":"Bottleneck","score":0.5242319703102112},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.5072562098503113},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.45856866240501404},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.45644599199295044},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.45378348231315613},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4518137574195862},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.44432199001312256},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.41664204001426697},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4026634693145752},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2108851671218872}],"concepts":[{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.7636299133300781},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.687751829624176},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.6379482746124268},{"id":"https://openalex.org/C79337645","wikidata":"https://www.wikidata.org/wiki/Q779824","display_name":"Outlier","level":2,"score":0.6118946075439453},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5935126543045044},{"id":"https://openalex.org/C111335779","wikidata":"https://www.wikidata.org/wiki/Q3454686","display_name":"Reduction (mathematics)","level":2,"score":0.5798361301422119},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.5612143278121948},{"id":"https://openalex.org/C2780513914","wikidata":"https://www.wikidata.org/wiki/Q18210350","display_name":"Bottleneck","level":2,"score":0.5242319703102112},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.5072562098503113},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.45856866240501404},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.45644599199295044},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.45378348231315613},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4518137574195862},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.44432199001312256},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.41664204001426697},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4026634693145752},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2108851671218872},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C149635348","wikidata":"https://www.wikidata.org/wiki/Q193040","display_name":"Embedded system","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},{"id":"https://openalex.org/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","level":1,"score":0.0},{"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/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.4018/japuc.2012100107","is_oa":false,"landing_page_url":"https://doi.org/10.4018/japuc.2012100107","pdf_url":null,"source":{"id":"https://openalex.org/S16161090","display_name":"International Journal of Advanced Pervasive and Ubiquitous Computing","issn_l":"1937-965X","issn":["1937-965X","1937-9668"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320424","host_organization_name":"IGI Global","host_organization_lineage":["https://openalex.org/P4310320424"],"host_organization_lineage_names":["IGI Global"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Advanced Pervasive and Ubiquitous Computing","raw_type":"journal-article"},{"id":"pmh:oai:RePEc:igg:japuc0:v:4:y:2012:i:4:p:63-73","is_oa":false,"landing_page_url":"http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/japuc.2012100107","pdf_url":null,"source":{"id":"https://openalex.org/S4306401271","display_name":"RePEc: Research Papers in Economics","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I77793887","host_organization_name":"Federal Reserve Bank of St. Louis","host_organization_lineage":["https://openalex.org/I77793887"],"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":"article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":4,"referenced_works":["https://openalex.org/W91477260","https://openalex.org/W1528620860","https://openalex.org/W1563088657","https://openalex.org/W2037679392"],"related_works":["https://openalex.org/W1657880117","https://openalex.org/W2595172197","https://openalex.org/W2127970246","https://openalex.org/W2084856301","https://openalex.org/W1001352512","https://openalex.org/W4382618745","https://openalex.org/W2885125400","https://openalex.org/W1989889224","https://openalex.org/W1987128138","https://openalex.org/W2743976221"],"abstract_inverted_index":{"There":[0],"has":[1],"become":[2],"a":[3,54],"bottleneck":[4],"to":[5,12,64],"use":[6],"support":[7,91],"vector":[8],"machine":[9],"(SVM)":[10],"due":[11],"the":[13,44,67,70,76,89,96,102,112,115,121,131,135,143,151],"problems":[14,31],"such":[15],"as":[16],"slow":[17],"learning":[18],"speed,":[19],"large":[20],"buffer":[21],"memory":[22],"requirement,":[23],"low":[24],"generalization":[25,122],"performance":[26,123],"and":[27,39,95,126,148],"so":[28],"on.":[29],"These":[30],"are":[32,93],"caused":[33],"by":[34],"large-scale":[35,59],"training":[36,60,71,116],"sample":[37,61,72,117],"set":[38,62,73,78],"outlier":[40,98],"data":[41,99],"immixed":[42,100],"in":[43,85,101],"other":[45,103],"class.":[46],"Aiming":[47],"at":[48],"these":[49],"problems,":[50],"this":[51,86],"paper":[52],"proposed":[53],"new":[55,87],"reduction":[56,137],"strategy":[57,138],"for":[58],"according":[63],"analyzing":[65],"on":[66,75],"structure":[68],"of":[69,109,114,124,146,158],"based":[74],"point":[77],"theory.":[79],"By":[80],"using":[81],"fuzzy":[82],"clustering":[83],"method":[84],"strategy,":[88],"potential":[90],"vectors":[92],"obtained":[94],"non-boundary":[97],"class":[104],"is":[105],"removed.":[106],"In":[107],"view":[108],"reducing":[110],"greatly":[111],"scale":[113],"set,":[118],"it":[119],"improves":[120],"SVM":[125,147],"effectively":[127],"avoids":[128],"over-learning.":[129],"Finally,":[130],"experimental":[132],"results":[133],"shown":[134],"given":[136],"can":[139],"not":[140],"only":[141],"reduce":[142],"train":[144,152],"samples":[145],"speed":[149],"up":[150],"process,":[153],"but":[154],"also":[155],"ensure":[156],"accuracy":[157],"classification.":[159]},"counts_by_year":[],"updated_date":"2026-06-11T06:19:23.411458","created_date":"2025-10-10T00:00:00"}