{"id":"https://openalex.org/W2970160020","doi":"https://doi.org/10.1142/s0219691319500589","title":"Multiple kernel learning by empirical target kernel","display_name":"Multiple kernel learning by empirical target kernel","publication_year":2019,"publication_date":"2019-08-26","ids":{"openalex":"https://openalex.org/W2970160020","doi":"https://doi.org/10.1142/s0219691319500589","mag":"2970160020"},"language":"en","primary_location":{"id":"doi:10.1142/s0219691319500589","is_oa":false,"landing_page_url":"https://doi.org/10.1142/s0219691319500589","pdf_url":null,"source":{"id":"https://openalex.org/S56986848","display_name":"International Journal of Wavelets Multiresolution and Information Processing","issn_l":"0219-6913","issn":["0219-6913","1793-690X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319815","host_organization_name":"World Scientific","host_organization_lineage":["https://openalex.org/P4310319815"],"host_organization_lineage_names":["World Scientific"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Wavelets, Multiresolution and Information Processing","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/A5101518889","display_name":"Peiyan Wang","orcid":"https://orcid.org/0000-0003-2317-2369"},"institutions":[{"id":"https://openalex.org/I125904092","display_name":"Shenyang Aerospace University","ror":"https://ror.org/02423gm04","country_code":"CN","type":"education","lineage":["https://openalex.org/I125904092"]},{"id":"https://openalex.org/I9842412","display_name":"Nanjing University of Aeronautics and Astronautics","ror":"https://ror.org/01scyh794","country_code":"CN","type":"education","lineage":["https://openalex.org/I9842412"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Peiyan Wang","raw_affiliation_strings":["College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, P. R. China","Knowledge Engineering Research Center, Shenyang Aerospace University, Shenyang, 110136, P. R. China"],"affiliations":[{"raw_affiliation_string":"College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, P. R. China","institution_ids":["https://openalex.org/I9842412"]},{"raw_affiliation_string":"Knowledge Engineering Research Center, Shenyang Aerospace University, Shenyang, 110136, P. R. China","institution_ids":["https://openalex.org/I125904092"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5102182084","display_name":"Dongfeng Cai","orcid":null},"institutions":[{"id":"https://openalex.org/I125904092","display_name":"Shenyang Aerospace University","ror":"https://ror.org/02423gm04","country_code":"CN","type":"education","lineage":["https://openalex.org/I125904092"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Dongfeng Cai","raw_affiliation_strings":["Knowledge Engineering Research Center, Shenyang Aerospace University, Shenyang, 110136, P. R. China"],"affiliations":[{"raw_affiliation_string":"Knowledge Engineering Research Center, Shenyang Aerospace University, Shenyang, 110136, P. R. China","institution_ids":["https://openalex.org/I125904092"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5101518889"],"corresponding_institution_ids":["https://openalex.org/I125904092","https://openalex.org/I9842412"],"apc_list":null,"apc_paid":null,"fwci":0.1012,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.43414159,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"18","issue":"02","first_page":"1950058","last_page":"1950058"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.9912999868392944,"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":0.9912999868392944,"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/T12676","display_name":"Machine Learning and ELM","score":0.9909999966621399,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9890000224113464,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/multiple-kernel-learning","display_name":"Multiple kernel learning","score":0.9199938178062439},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.7948157787322998},{"id":"https://openalex.org/keywords/tree-kernel","display_name":"Tree kernel","score":0.7765069007873535},{"id":"https://openalex.org/keywords/kernel-embedding-of-distributions","display_name":"Kernel embedding of distributions","score":0.6864490509033203},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6072749495506287},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5799557566642761},{"id":"https://openalex.org/keywords/radial-basis-function-kernel","display_name":"Radial basis function kernel","score":0.5760971903800964},{"id":"https://openalex.org/keywords/string-kernel","display_name":"String kernel","score":0.5754549503326416},{"id":"https://openalex.org/keywords/kernel-method","display_name":"Kernel method","score":0.5668743252754211},{"id":"https://openalex.org/keywords/polynomial-kernel","display_name":"Polynomial kernel","score":0.5621553659439087},{"id":"https://openalex.org/keywords/variable-kernel-density-estimation","display_name":"Variable kernel density estimation","score":0.5545576214790344},{"id":"https://openalex.org/keywords/kernel-principal-component-analysis","display_name":"Kernel principal component analysis","score":0.5232638120651245},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5163763761520386},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.43601739406585693},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.42672374844551086},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3656960129737854},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.33453571796417236},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.1887190341949463}],"concepts":[{"id":"https://openalex.org/C2776879701","wikidata":"https://www.wikidata.org/wiki/Q25048660","display_name":"Multiple kernel learning","level":4,"score":0.9199938178062439},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.7948157787322998},{"id":"https://openalex.org/C140417398","wikidata":"https://www.wikidata.org/wiki/Q16933942","display_name":"Tree kernel","level":5,"score":0.7765069007873535},{"id":"https://openalex.org/C134517425","wikidata":"https://www.wikidata.org/wiki/Q16000131","display_name":"Kernel embedding of distributions","level":4,"score":0.6864490509033203},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6072749495506287},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5799557566642761},{"id":"https://openalex.org/C75866337","wikidata":"https://www.wikidata.org/wiki/Q7280263","display_name":"Radial basis function kernel","level":4,"score":0.5760971903800964},{"id":"https://openalex.org/C55851704","wikidata":"https://www.wikidata.org/wiki/Q7623983","display_name":"String kernel","level":5,"score":0.5754549503326416},{"id":"https://openalex.org/C122280245","wikidata":"https://www.wikidata.org/wiki/Q620622","display_name":"Kernel method","level":3,"score":0.5668743252754211},{"id":"https://openalex.org/C160446489","wikidata":"https://www.wikidata.org/wiki/Q7226642","display_name":"Polynomial kernel","level":4,"score":0.5621553659439087},{"id":"https://openalex.org/C195699287","wikidata":"https://www.wikidata.org/wiki/Q7915722","display_name":"Variable kernel density estimation","level":4,"score":0.5545576214790344},{"id":"https://openalex.org/C182335926","wikidata":"https://www.wikidata.org/wiki/Q17093020","display_name":"Kernel principal component analysis","level":4,"score":0.5232638120651245},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5163763761520386},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.43601739406585693},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.42672374844551086},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3656960129737854},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.33453571796417236},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.1887190341949463},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0},{"id":"https://openalex.org/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1142/s0219691319500589","is_oa":false,"landing_page_url":"https://doi.org/10.1142/s0219691319500589","pdf_url":null,"source":{"id":"https://openalex.org/S56986848","display_name":"International Journal of Wavelets Multiresolution and Information Processing","issn_l":"0219-6913","issn":["0219-6913","1793-690X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319815","host_organization_name":"World Scientific","host_organization_lineage":["https://openalex.org/P4310319815"],"host_organization_lineage_names":["World Scientific"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Wavelets, Multiresolution and Information Processing","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2483438776","display_name":null,"funder_award_id":"20170540705","funder_id":"https://openalex.org/F4320323086","funder_display_name":"Natural Science Foundation of Liaoning Province"},{"id":"https://openalex.org/G5973676861","display_name":null,"funder_award_id":"61402299","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320323086","display_name":"Natural Science Foundation of Liaoning Province","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W1920328734","https://openalex.org/W1984838032","https://openalex.org/W1990274827","https://openalex.org/W2014069437","https://openalex.org/W2022772671","https://openalex.org/W2031823405","https://openalex.org/W2048758983","https://openalex.org/W2048896634","https://openalex.org/W2060166505","https://openalex.org/W2108710077","https://openalex.org/W2109743529","https://openalex.org/W2115521056","https://openalex.org/W2127069950","https://openalex.org/W2131154631","https://openalex.org/W2144299089","https://openalex.org/W2145394798","https://openalex.org/W2148603752","https://openalex.org/W2154462399","https://openalex.org/W2287118524","https://openalex.org/W2290701356","https://openalex.org/W2891656133"],"related_works":["https://openalex.org/W3013206934","https://openalex.org/W4291669689","https://openalex.org/W2071590642","https://openalex.org/W4323338655","https://openalex.org/W1969447452","https://openalex.org/W2806943235","https://openalex.org/W1976730005","https://openalex.org/W2141199622","https://openalex.org/W2898882859","https://openalex.org/W2055608878"],"abstract_inverted_index":{"Multiple":[0],"kernel":[1,31,50,66,76,131,158,183,187],"learning":[2,6,40],"(MKL)":[3],"aims":[4],"at":[5],"an":[7,15],"optimal":[8],"combination":[9],"of":[10,53,58,106,111,149,233],"base":[11,119],"kernels":[12,83,136],"with":[13],"which":[14,88,169],"appropriate":[16],"hypothesis":[17],"is":[18,51,109,115,159,170,188],"determined":[19],"on":[20,214],"the":[21,36,54,65,70,91,104,125,128,142,166,177,206,229,234],"training":[22,112,167],"data.":[23],"MKL":[24,60,217],"has":[25],"its":[26],"flexibility":[27],"featured":[28],"by":[29,68,118],"automated":[30],"learning,":[32],"and":[33,45,77,97,101,114,172,197,231],"also":[34],"reflects":[35],"fact":[37],"that":[38,222],"typical":[39],"problems":[41],"often":[42],"involve":[43],"multiple":[44],"heterogeneous":[46],"data":[47],"sources.":[48],"Target":[49],"one":[52],"most":[55],"important":[56],"parts":[57],"many":[59],"methods.":[61,236],"These":[62],"methods":[63],"find":[64],"weights":[67],"maximizing":[69,124],"similarity":[71,126],"or":[72],"alignment":[73],"between":[74],"weighted":[75],"target":[78,82,93,130,153,157,182,186,209],"kernel.":[79,210],"The":[80,219],"existing":[81],"implement":[84,194],"a":[85,122,151,155],"global":[86,129,152],"manner,":[87],"(1)":[89],"defines":[90],"same":[92],"value":[94],"for":[95,161],"closer":[96],"farther":[98],"sample":[99,163,178],"pairs,":[100],"inappropriately":[102],"neglects":[103],"variation":[105],"samples;":[107],"(2)":[108],"independent":[110],"data,":[113,168],"hardly":[116],"approximated":[117],"kernels.":[120],"As":[121],"result,":[123],"to":[127,174,193,203],"could":[132],"make":[133],"these":[134],"pre-specified":[135],"less":[137],"effectively":[138],"utilized,":[139],"further":[140],"reducing":[141],"classification":[143],"performance.":[144],"In":[145],"this":[146,191,195],"paper,":[147],"instead":[148],"defining":[150],"kernel,":[154],"localized":[156],"calculated":[160],"each":[162],"pair":[164],"from":[165],"flexible":[171],"able":[173],"well":[175],"handle":[176],"variations.":[179],"A":[180],"new":[181],"named":[184],"empirical":[185,208],"proposed":[189,207,235],"in":[190],"research":[192],"idea,":[196],"three":[198],"corresponding":[199],"algorithms":[200,224],"are":[201,212],"designed":[202],"efficiently":[204],"utilize":[205],"Experiments":[211],"conducted":[213],"four":[215],"challenging":[216],"problems.":[218],"results":[220],"show":[221],"our":[223],"outperform":[225],"other":[226],"methods,":[227],"verifying":[228],"effectiveness":[230],"superiority":[232]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}