{"id":"https://openalex.org/W1972833449","doi":"https://doi.org/10.1109/geoinformatics.2010.5567766","title":"A study on improving training speed in support vector machine","display_name":"A study on improving training speed in support vector machine","publication_year":2010,"publication_date":"2010-06-01","ids":{"openalex":"https://openalex.org/W1972833449","doi":"https://doi.org/10.1109/geoinformatics.2010.5567766","mag":"1972833449"},"language":"en","primary_location":{"id":"doi:10.1109/geoinformatics.2010.5567766","is_oa":false,"landing_page_url":"https://doi.org/10.1109/geoinformatics.2010.5567766","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2010 18th International Conference on Geoinformatics","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/A5023337666","display_name":"Jin-Tsong Hwang","orcid":null},"institutions":[{"id":"https://openalex.org/I99613584","display_name":"National Taipei University","ror":"https://ror.org/03e29r284","country_code":"TW","type":"education","lineage":["https://openalex.org/I99613584"]}],"countries":["TW"],"is_corresponding":true,"raw_author_name":"Jin-Tsong Hwang","raw_affiliation_strings":["Department of Real Estate and Built Environment, National Taipei University, Taipei, Taiwan","Department of Real Estate and Built Environment, National Taipei University, No. 151, University Rd., San Shia, Taipei County, Taiwan"],"affiliations":[{"raw_affiliation_string":"Department of Real Estate and Built Environment, National Taipei University, Taipei, Taiwan","institution_ids":["https://openalex.org/I99613584"]},{"raw_affiliation_string":"Department of Real Estate and Built Environment, National Taipei University, No. 151, University Rd., San Shia, Taipei County, Taiwan","institution_ids":["https://openalex.org/I99613584"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5023337666"],"corresponding_institution_ids":["https://openalex.org/I99613584"],"apc_list":null,"apc_paid":null,"fwci":0.3187,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.55902655,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"13","issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.9973000288009644,"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":0.9973000288009644,"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/T10689","display_name":"Remote-Sensing Image Classification","score":0.9951000213623047,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"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/T13890","display_name":"Remote Sensing and Land Use","score":0.9930999875068665,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/hyperplane","display_name":"Hyperplane","score":0.7837945818901062},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.7835198640823364},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.681191623210907},{"id":"https://openalex.org/keywords/feature-vector","display_name":"Feature vector","score":0.6327409744262695},{"id":"https://openalex.org/keywords/ellipsoid","display_name":"Ellipsoid","score":0.627171516418457},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5722382068634033},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.549705982208252},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5495724081993103},{"id":"https://openalex.org/keywords/standard-deviation","display_name":"Standard deviation","score":0.5325314998626709},{"id":"https://openalex.org/keywords/training","display_name":"Training (meteorology)","score":0.4651215076446533},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.4617471992969513},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.45144790410995483},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.4432581961154938},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.44131535291671753},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.42994561791419983},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4259946346282959},{"id":"https://openalex.org/keywords/variance","display_name":"Variance (accounting)","score":0.42308321595191956},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.23252776265144348},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.23040157556533813},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.18682241439819336}],"concepts":[{"id":"https://openalex.org/C68693459","wikidata":"https://www.wikidata.org/wiki/Q657586","display_name":"Hyperplane","level":2,"score":0.7837945818901062},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.7835198640823364},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.681191623210907},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.6327409744262695},{"id":"https://openalex.org/C57489055","wikidata":"https://www.wikidata.org/wiki/Q190046","display_name":"Ellipsoid","level":2,"score":0.627171516418457},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5722382068634033},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.549705982208252},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5495724081993103},{"id":"https://openalex.org/C22679943","wikidata":"https://www.wikidata.org/wiki/Q159375","display_name":"Standard deviation","level":2,"score":0.5325314998626709},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.4651215076446533},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.4617471992969513},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.45144790410995483},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.4432581961154938},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.44131535291671753},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.42994561791419983},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4259946346282959},{"id":"https://openalex.org/C196083921","wikidata":"https://www.wikidata.org/wiki/Q7915758","display_name":"Variance (accounting)","level":2,"score":0.42308321595191956},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.23252776265144348},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.23040157556533813},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.18682241439819336},{"id":"https://openalex.org/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"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/C121955636","wikidata":"https://www.wikidata.org/wiki/Q4116214","display_name":"Accounting","level":1,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","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/C1276947","wikidata":"https://www.wikidata.org/wiki/Q333","display_name":"Astronomy","level":1,"score":0.0},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","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/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/geoinformatics.2010.5567766","is_oa":false,"landing_page_url":"https://doi.org/10.1109/geoinformatics.2010.5567766","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2010 18th International Conference on Geoinformatics","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":25,"referenced_works":["https://openalex.org/W628438000","https://openalex.org/W634710820","https://openalex.org/W1551209770","https://openalex.org/W1621799579","https://openalex.org/W2095975892","https://openalex.org/W2112545207","https://openalex.org/W2136251662","https://openalex.org/W2137557016","https://openalex.org/W2139212933","https://openalex.org/W2144414190","https://openalex.org/W2148980227","https://openalex.org/W2153635508","https://openalex.org/W2167106053","https://openalex.org/W2172000360","https://openalex.org/W2303501228","https://openalex.org/W2348406777","https://openalex.org/W2478493250","https://openalex.org/W3120421331","https://openalex.org/W4316465291","https://openalex.org/W6620512211","https://openalex.org/W6632758585","https://openalex.org/W6680634009","https://openalex.org/W6681291431","https://openalex.org/W6849195701","https://openalex.org/W7039725006"],"related_works":["https://openalex.org/W2369655046","https://openalex.org/W2019467317","https://openalex.org/W1585519779","https://openalex.org/W1965515989","https://openalex.org/W4376167435","https://openalex.org/W2088737283","https://openalex.org/W1999587863","https://openalex.org/W1929902734","https://openalex.org/W2028729143","https://openalex.org/W2320105591"],"abstract_inverted_index":{"In":[0,86,97],"a":[1,37,54,91,102],"SVM":[2,44,50],"classifier,":[3],"the":[4,10,15,25,32,43,83,140,145],"training":[5,28,33,84,93,137,166,192],"speed":[6,34],"is":[7,35,51,128],"sensitive":[8],"to":[9,39,119],"quantity":[11],"of":[12,17,27,49,57,68,108,111,126,136,142,150,161,175],"dataset.":[13],"Therefore,":[14],"methodology":[16],"choosing":[18,143],"some":[19],"useful":[20,121],"data":[21,29,45,152,162,193],"that":[22],"can":[23],"decrease":[24],"number":[26,56,174],"and":[30,158,180],"accelerate":[31],"usually":[36],"topic":[38],"be":[40],"discussed":[41],"on":[42,147],"process.":[46,85,167],"The":[47,183],"hyperplane":[48],"constructed":[52],"by":[53,130,173],"small":[55],"vectors.":[58],"These":[59],"vectors,":[60],"whose":[61],"locations":[62,146],"are":[63,70,153,163],"distributed":[64],"in":[65,116],"other":[66],"kind":[67],"classes,":[69],"not":[71],"only":[72],"helpless":[73],"for":[74,95,165,190],"corrected":[75],"classification":[76,188],"but":[77],"also":[78],"increase":[79],"computation":[80],"loading":[81],"during":[82],"addition,":[87],"it":[88],"may":[89],"generate":[90],"weak":[92],"model":[94],"classification.":[96],"this":[98],"paper,":[99],"we":[100],"propose":[101],"new":[103],"method":[104,185],"using":[105],"ellipsoid":[106,124],"region":[107,125],"n":[109],"times":[110],"distribution":[112],"standard":[113],"deviation":[114],"(\u03c3)":[115],"feature":[117,148],"space":[118,149,157],"choose":[120],"data.":[122,138],"Each":[123],"class":[127],"formed":[129],"variance":[131],"co-variance":[132],"matrix":[133],"among":[134],"multi-bands":[135],"After":[139],"course":[141],"data,":[144],"selected":[151,176],"mapped":[154],"into":[155],"image":[156],"these":[159],"kinds":[160],"collected":[164],"Experimental":[168],"results":[169,189],"assessment":[170],"was":[171],"adopted":[172],"samples,":[177],"overall":[178],"accuracy,":[179],"CPU":[181],"time.":[182],"proposed":[184],"produces":[186],"promising":[187],"reducing":[191],"analysis":[194],"problems.":[195]},"counts_by_year":[{"year":2013,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}