{"id":"https://openalex.org/W2902307213","doi":"https://doi.org/10.1109/icpr.2018.8545294","title":"An Online Kernel Selection Wrapper via Multi-Armed Bandit Model","display_name":"An Online Kernel Selection Wrapper via Multi-Armed Bandit Model","publication_year":2018,"publication_date":"2018-08-01","ids":{"openalex":"https://openalex.org/W2902307213","doi":"https://doi.org/10.1109/icpr.2018.8545294","mag":"2902307213"},"language":"en","primary_location":{"id":"doi:10.1109/icpr.2018.8545294","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icpr.2018.8545294","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 24th International Conference on Pattern Recognition (ICPR)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5002025150","display_name":"Junfan Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Junfan Li","raw_affiliation_strings":["School of Computer Science and Technology, Tianjin University, Tianjin, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, Tianjin University, Tianjin, China","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5089843351","display_name":"Shizhong Liao","orcid":"https://orcid.org/0000-0003-0594-7116"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shizhong Liao","raw_affiliation_strings":["School of Computer Science and Technology, Tianjin University, Tianjin, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, Tianjin University, Tianjin, China","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":"51","issue":null,"first_page":"1307","last_page":"1312"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12101","display_name":"Advanced Bandit Algorithms Research","score":0.9997000098228455,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T12101","display_name":"Advanced Bandit Algorithms Research","score":0.9997000098228455,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T12676","display_name":"Machine Learning and ELM","score":0.9976000189781189,"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":0.9883999824523926,"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/kernel","display_name":"Kernel (algebra)","score":0.7794477939605713},{"id":"https://openalex.org/keywords/regret","display_name":"Regret","score":0.6978862285614014},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6903185844421387},{"id":"https://openalex.org/keywords/tree-kernel","display_name":"Tree kernel","score":0.5875107645988464},{"id":"https://openalex.org/keywords/radial-basis-function-kernel","display_name":"Radial basis function kernel","score":0.5716054439544678},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5700017809867859},{"id":"https://openalex.org/keywords/multiple-kernel-learning","display_name":"Multiple kernel learning","score":0.5652425289154053},{"id":"https://openalex.org/keywords/variable-kernel-density-estimation","display_name":"Variable kernel density estimation","score":0.5595972537994385},{"id":"https://openalex.org/keywords/kernel-embedding-of-distributions","display_name":"Kernel embedding of distributions","score":0.5472121834754944},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.5358598828315735},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4814016819000244},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4684804677963257},{"id":"https://openalex.org/keywords/kernel-method","display_name":"Kernel method","score":0.4497048556804657},{"id":"https://openalex.org/keywords/polynomial-kernel","display_name":"Polynomial kernel","score":0.431692510843277},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.41193628311157227},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.32015255093574524},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2128995954990387},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.18975606560707092}],"concepts":[{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.7794477939605713},{"id":"https://openalex.org/C50817715","wikidata":"https://www.wikidata.org/wiki/Q79895177","display_name":"Regret","level":2,"score":0.6978862285614014},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6903185844421387},{"id":"https://openalex.org/C140417398","wikidata":"https://www.wikidata.org/wiki/Q16933942","display_name":"Tree kernel","level":5,"score":0.5875107645988464},{"id":"https://openalex.org/C75866337","wikidata":"https://www.wikidata.org/wiki/Q7280263","display_name":"Radial basis function kernel","level":4,"score":0.5716054439544678},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5700017809867859},{"id":"https://openalex.org/C2776879701","wikidata":"https://www.wikidata.org/wiki/Q25048660","display_name":"Multiple kernel learning","level":4,"score":0.5652425289154053},{"id":"https://openalex.org/C195699287","wikidata":"https://www.wikidata.org/wiki/Q7915722","display_name":"Variable kernel density estimation","level":4,"score":0.5595972537994385},{"id":"https://openalex.org/C134517425","wikidata":"https://www.wikidata.org/wiki/Q16000131","display_name":"Kernel embedding of distributions","level":4,"score":0.5472121834754944},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.5358598828315735},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4814016819000244},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4684804677963257},{"id":"https://openalex.org/C122280245","wikidata":"https://www.wikidata.org/wiki/Q620622","display_name":"Kernel method","level":3,"score":0.4497048556804657},{"id":"https://openalex.org/C160446489","wikidata":"https://www.wikidata.org/wiki/Q7226642","display_name":"Polynomial kernel","level":4,"score":0.431692510843277},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.41193628311157227},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.32015255093574524},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2128995954990387},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.18975606560707092},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","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/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icpr.2018.8545294","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icpr.2018.8545294","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 24th International Conference on Pattern Recognition (ICPR)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.4399999976158142,"id":"https://metadata.un.org/sdg/16","display_name":"Peace, Justice and strong institutions"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":27,"referenced_works":["https://openalex.org/W1492499319","https://openalex.org/W1575029535","https://openalex.org/W2049934117","https://openalex.org/W2050522395","https://openalex.org/W2077902449","https://openalex.org/W2101379030","https://openalex.org/W2103736488","https://openalex.org/W2114615420","https://openalex.org/W2121985812","https://openalex.org/W2125335195","https://openalex.org/W2132087961","https://openalex.org/W2136687243","https://openalex.org/W2153635508","https://openalex.org/W2161603851","https://openalex.org/W2408432900","https://openalex.org/W2608738262","https://openalex.org/W2741352581","https://openalex.org/W2950929549","https://openalex.org/W2963334428","https://openalex.org/W3120740533","https://openalex.org/W6634542218","https://openalex.org/W6675774749","https://openalex.org/W6678237272","https://openalex.org/W6678613826","https://openalex.org/W6680187362","https://openalex.org/W6714150371","https://openalex.org/W6736891279"],"related_works":["https://openalex.org/W4291669689","https://openalex.org/W1969447452","https://openalex.org/W4282562207","https://openalex.org/W2071590642","https://openalex.org/W2806943235","https://openalex.org/W4323338655","https://openalex.org/W2141199622","https://openalex.org/W2898882859","https://openalex.org/W2371064519","https://openalex.org/W1976730005"],"abstract_inverted_index":{"Online":[0],"kernel":[1,7,15,21,32,35,59,71,91,110,161,169,191,203],"selection":[2,22,60,170,192],"is":[3,41,153],"critical":[4],"to":[5,43,88,111,124,172,196],"online":[6,14,20,58,90,160,168,190],"learning,":[8],"but":[9],"most":[10],"of":[11,78,98,114,148,158,176,200,216],"the":[12,19,44,63,95,102,105,115,125,130,136,139,142,146,149,156,159,174,177,183,197,201,205,214,217],"existing":[13],"learning":[16,92,162],"methods":[17],"ignore":[18],"process,":[23],"and":[24,29,47,84,119,180],"instead":[25],"they":[26],"empirically":[27],"preset":[28],"fix":[30],"a":[31,70,76,166,187],"or":[33],"adjust":[34],"parameters":[36],"by":[37,129,155],"gradient":[38],"descent,":[39],"which":[40,67,152],"sensitive":[42],"initial":[45],"setting":[46],"has":[48],"no":[49],"theoretical":[50,82],"guarantee.":[51],"In":[52,101,135],"this":[53],"work,":[54],"we":[55],"propose":[56,165],"an":[57,112,121],"wrapper":[61,96,106,140,185],"via":[62],"multi-armed":[64,116],"bandit":[65,117],"model,":[66,118],"can":[68,85],"select":[69],"at":[72,132],"each":[73,108,133],"round":[74],"from":[75],"set":[77],"candidate":[79,109],"kernels":[80],"with":[81,194],"guarantee":[83],"be":[86],"applied":[87],"any":[89],"model.":[93],"Specifically,":[94],"consists":[97],"two":[99],"layers.":[100],"outer":[103],"layer,":[104,138],"corresponds":[107],"arm":[113,122],"chooses":[120],"according":[123,145],"probability":[126,143],"distribution":[127,144],"maintained":[128],"model":[131],"round.":[134],"inner":[137],"updates":[141],"loss":[147,199],"selected":[150],"arm,":[151],"incurred":[154],"prediction":[157],"algorithm.":[163],"We":[164],"new":[167],"regret":[171,193],"measure":[173],"performance":[175],"proposed":[178,184,218],"wrapper,":[179],"prove":[181],"that":[182],"enjoys":[186],"sub-linear":[188],"expected":[189],"respect":[195],"cumulative":[198],"optimal":[202],"among":[204],"candidates":[206],"kernels.":[207],"Experimental":[208],"results":[209],"on":[210],"benchmark":[211],"datasets":[212],"demonstrate":[213],"effectiveness":[215],"wrapper.":[219]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
