{"id":"https://openalex.org/W3034519502","doi":"https://doi.org/10.1109/sisy47553.2019.9111587","title":"Difference based query strategies in active learning","display_name":"Difference based query strategies in active learning","publication_year":2019,"publication_date":"2019-09-01","ids":{"openalex":"https://openalex.org/W3034519502","doi":"https://doi.org/10.1109/sisy47553.2019.9111587","mag":"3034519502"},"language":"en","primary_location":{"id":"doi:10.1109/sisy47553.2019.9111587","is_oa":false,"landing_page_url":"https://doi.org/10.1109/sisy47553.2019.9111587","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE 17th International Symposium on Intelligent Systems and Informatics (SISY)","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/A5046406057","display_name":"D\u00e1vid Papp","orcid":"https://orcid.org/0000-0002-8814-2745"},"institutions":[{"id":"https://openalex.org/I29770179","display_name":"Budapest University of Technology and Economics","ror":"https://ror.org/02w42ss30","country_code":"HU","type":"education","lineage":["https://openalex.org/I29770179"]}],"countries":["HU"],"is_corresponding":true,"raw_author_name":"D\u00e1vid Papp","raw_affiliation_strings":["Budapest University of Technology and Economics,Department of Telecommunications and Media Informatics,Budapest,Hungary","Department of Telecommunications and Media Informatics, Budapest University of Technology and Economics, Budapest, Hungary"],"affiliations":[{"raw_affiliation_string":"Budapest University of Technology and Economics,Department of Telecommunications and Media Informatics,Budapest,Hungary","institution_ids":["https://openalex.org/I29770179"]},{"raw_affiliation_string":"Department of Telecommunications and Media Informatics, Budapest University of Technology and Economics, Budapest, Hungary","institution_ids":["https://openalex.org/I29770179"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5076485961","display_name":"G\u00e1bor Sz\u00fccs","orcid":"https://orcid.org/0000-0002-5781-1088"},"institutions":[{"id":"https://openalex.org/I29770179","display_name":"Budapest University of Technology and Economics","ror":"https://ror.org/02w42ss30","country_code":"HU","type":"education","lineage":["https://openalex.org/I29770179"]}],"countries":["HU"],"is_corresponding":false,"raw_author_name":"G\u00e1bor Sz\u0171cs","raw_affiliation_strings":["Budapest University of Technology and Economics,Department of Telecommunications and Media Informatics,Budapest,Hungary","Department of Telecommunications and Media Informatics, Budapest University of Technology and Economics, Budapest, Hungary"],"affiliations":[{"raw_affiliation_string":"Budapest University of Technology and Economics,Department of Telecommunications and Media Informatics,Budapest,Hungary","institution_ids":["https://openalex.org/I29770179"]},{"raw_affiliation_string":"Department of Telecommunications and Media Informatics, Budapest University of Technology and Economics, Budapest, Hungary","institution_ids":["https://openalex.org/I29770179"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5017417574","display_name":"Zsolt Knoll","orcid":null},"institutions":[{"id":"https://openalex.org/I29770179","display_name":"Budapest University of Technology and Economics","ror":"https://ror.org/02w42ss30","country_code":"HU","type":"education","lineage":["https://openalex.org/I29770179"]},{"id":"https://openalex.org/I4210121644","display_name":"HUN-REN Balaton Limnological Research Institute","ror":"https://ror.org/02pnhwp93","country_code":"HU","type":"facility","lineage":["https://openalex.org/I4210121644","https://openalex.org/I4387152226"]}],"countries":["HU"],"is_corresponding":false,"raw_author_name":"Zsolt Knoll","raw_affiliation_strings":["Budapest University of Technology and Economics,Balatonf&#x00FC;red Student Research Group,Balatonf&#x00FC;red,Hungary","Balatonf\u00fcred Student Research Group, Budapest University of Technology and Economics, Balatonf\u00fcred, Hungary"],"affiliations":[{"raw_affiliation_string":"Budapest University of Technology and Economics,Balatonf&#x00FC;red Student Research Group,Balatonf&#x00FC;red,Hungary","institution_ids":["https://openalex.org/I4210121644"]},{"raw_affiliation_string":"Balatonf\u00fcred Student Research Group, Budapest University of Technology and Economics, Balatonf\u00fcred, Hungary","institution_ids":["https://openalex.org/I29770179"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5046406057"],"corresponding_institution_ids":["https://openalex.org/I29770179"],"apc_list":null,"apc_paid":null,"fwci":0.28,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.68346579,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":93,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"35","last_page":"40"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12072","display_name":"Machine Learning and Algorithms","score":0.9998000264167786,"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"}},"topics":[{"id":"https://openalex.org/T12072","display_name":"Machine Learning and Algorithms","score":0.9998000264167786,"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/T12535","display_name":"Machine Learning and Data Classification","score":0.9855999946594238,"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9807999730110168,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.7032305002212524},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6421335935592651},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.6026625633239746},{"id":"https://openalex.org/keywords/significant-difference","display_name":"Significant difference","score":0.5605615973472595},{"id":"https://openalex.org/keywords/measure","display_name":"Measure (data warehouse)","score":0.4579774737358093},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.2960970401763916},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2817899286746979},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.25182321667671204}],"concepts":[{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.7032305002212524},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6421335935592651},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.6026625633239746},{"id":"https://openalex.org/C3018023364","wikidata":"https://www.wikidata.org/wiki/Q425265","display_name":"Significant difference","level":2,"score":0.5605615973472595},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.4579774737358093},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2960970401763916},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2817899286746979},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.25182321667671204},{"id":"https://openalex.org/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","level":1,"score":0.0},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.0},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/sisy47553.2019.9111587","is_oa":false,"landing_page_url":"https://doi.org/10.1109/sisy47553.2019.9111587","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE 17th International Symposium on Intelligent Systems and Informatics (SISY)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Quality Education","score":0.5299999713897705,"id":"https://metadata.un.org/sdg/4"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":24,"referenced_works":["https://openalex.org/W1606858007","https://openalex.org/W1993615002","https://openalex.org/W2008989859","https://openalex.org/W2026566343","https://openalex.org/W2034747098","https://openalex.org/W2087347434","https://openalex.org/W2128678390","https://openalex.org/W2132914434","https://openalex.org/W2147238549","https://openalex.org/W2151103935","https://openalex.org/W2155904486","https://openalex.org/W2166049352","https://openalex.org/W2235812087","https://openalex.org/W2342049278","https://openalex.org/W2555903409","https://openalex.org/W2748386424","https://openalex.org/W2750714283","https://openalex.org/W2765885542","https://openalex.org/W2798542376","https://openalex.org/W2809113079","https://openalex.org/W2884929782","https://openalex.org/W4239510810","https://openalex.org/W4300672471","https://openalex.org/W6679227803"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W4255837520","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W4396696052","https://openalex.org/W4402327032"],"abstract_inverted_index":{"In":[0],"this":[1,43,82,127],"paper":[2],"two":[3],"active":[4],"learning":[5,12],"methods":[6,26,169],"are":[7,45],"proposed":[8],"in":[9,143,170],"the":[10,24,40,50,54,60,72,75,97,116,119,133,140,147,160,167],"machine":[11],"literature,":[13],"both":[14,158],"of":[15,23,42,53,62,74,118,121,126,146,159],"them":[16],"based":[17,162],"on":[18,153],"difference":[19,28,37,76,87,99,120,145,161],"calculation":[20],"idea.":[21],"One":[22],"new":[25,66,95],"is":[27,69,79,130],"sampling":[29,77,112,163],"query":[30,89,132,164],"strategy.":[31],"This":[32,104],"strategy":[33,78,90,105,129],"calculates":[34],"a":[35,65,94],"novel":[36],"list":[38,44],"and":[39,71,110],"elements":[41],"then":[46],"able":[47],"to":[48,80,131],"influence":[49],"uncertainty":[51,111,122],"measure":[52],"appropriate":[55],"unlabelled":[56],"instance.":[57],"By":[58],"taking":[59,115],"ratio":[61],"these":[63],"measures":[64],"informativeness":[67],"metric":[68,100],"defined,":[70],"aim":[73,125],"minimize":[81],"ratio.":[83],"Besides":[84],"that,":[85],"expected":[86,107],"change":[88,109,142],"was":[91],"developed":[92],"using":[93],"metric,":[96],"global":[98,144],"for":[101],"each":[102],"step.":[103,149],"combines":[106],"model":[108],"strategies":[113,165],"by":[114],"expectation":[117],"values.":[123],"The":[124,150],"combined":[128],"instance":[134],"that":[135,157],"will":[136],"most":[137],"likely":[138],"result":[139],"greatest":[141],"next":[148],"experimental":[151],"results":[152],"image":[154],"dataset":[155],"show":[156],"surpass":[166],"competitive":[168],"literature.":[171]},"counts_by_year":[{"year":2021,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
