{"id":"https://openalex.org/W2008447695","doi":"https://doi.org/10.1145/2063576.2063601","title":"Query sampling for learning data fusion","display_name":"Query sampling for learning data fusion","publication_year":2011,"publication_date":"2011-10-24","ids":{"openalex":"https://openalex.org/W2008447695","doi":"https://doi.org/10.1145/2063576.2063601","mag":"2008447695"},"language":"en","primary_location":{"id":"doi:10.1145/2063576.2063601","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2063576.2063601","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 20th ACM international conference on Information and knowledge management","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/A5003740048","display_name":"Ting-Chu Lin","orcid":null},"institutions":[{"id":"https://openalex.org/I16733864","display_name":"National Taiwan University","ror":"https://ror.org/05bqach95","country_code":"TW","type":"education","lineage":["https://openalex.org/I16733864"]}],"countries":["TW"],"is_corresponding":true,"raw_author_name":"Ting-Chu Lin","raw_affiliation_strings":["Department of Computer Science and Information Engineering National Taiwan University, Taiwan, Taipei, Taiwan Roc","Department of Computer Science and Information Engineering, National Taiwan University, Taiwan, Taipei, Taiwan Roc#TAB#"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Information Engineering National Taiwan University, Taiwan, Taipei, Taiwan Roc","institution_ids":["https://openalex.org/I16733864"]},{"raw_affiliation_string":"Department of Computer Science and Information Engineering, National Taiwan University, Taiwan, Taipei, Taiwan Roc#TAB#","institution_ids":["https://openalex.org/I16733864"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5071684622","display_name":"Pu\u2010Jen Cheng","orcid":"https://orcid.org/0000-0001-5892-0385"},"institutions":[{"id":"https://openalex.org/I16733864","display_name":"National Taiwan University","ror":"https://ror.org/05bqach95","country_code":"TW","type":"education","lineage":["https://openalex.org/I16733864"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Pu-Jen Cheng","raw_affiliation_strings":["Department of Computer Science and Information Engineering National Taiwan University, Taiwan, Taipei, Taiwan Roc","Department of Computer Science and Information Engineering, National Taiwan University, Taiwan, Taipei, Taiwan Roc#TAB#"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Information Engineering National Taiwan University, Taiwan, Taipei, Taiwan Roc","institution_ids":["https://openalex.org/I16733864"]},{"raw_affiliation_string":"Department of Computer Science and Information Engineering, National Taiwan University, Taiwan, Taipei, Taiwan Roc#TAB#","institution_ids":["https://openalex.org/I16733864"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5003740048"],"corresponding_institution_ids":["https://openalex.org/I16733864"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.08279087,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"141","last_page":"146"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9987000226974487,"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9987000226974487,"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/T12072","display_name":"Machine Learning and Algorithms","score":0.9980000257492065,"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/T10824","display_name":"Image Retrieval and Classification Techniques","score":0.9970999956130981,"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/computer-science","display_name":"Computer science","score":0.852138340473175},{"id":"https://openalex.org/keywords/merge","display_name":"Merge (version control)","score":0.7413996458053589},{"id":"https://openalex.org/keywords/boosting","display_name":"Boosting (machine learning)","score":0.7107065320014954},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.5400453805923462},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5165807008743286},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.502047061920166},{"id":"https://openalex.org/keywords/sensor-fusion","display_name":"Sensor fusion","score":0.5019259452819824},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.4951888620853424},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.46057984232902527},{"id":"https://openalex.org/keywords/fusion","display_name":"Fusion","score":0.4200553894042969},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.36008045077323914}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.852138340473175},{"id":"https://openalex.org/C197129107","wikidata":"https://www.wikidata.org/wiki/Q1921621","display_name":"Merge (version control)","level":2,"score":0.7413996458053589},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.7107065320014954},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.5400453805923462},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5165807008743286},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.502047061920166},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.5019259452819824},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.4951888620853424},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.46057984232902527},{"id":"https://openalex.org/C158525013","wikidata":"https://www.wikidata.org/wiki/Q2593739","display_name":"Fusion","level":2,"score":0.4200553894042969},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.36008045077323914},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","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/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.0},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/2063576.2063601","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2063576.2063601","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 20th ACM international conference on Information and knowledge management","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W1511363262","https://openalex.org/W1540841176","https://openalex.org/W1557418592","https://openalex.org/W1574528120","https://openalex.org/W1576980620","https://openalex.org/W1581062453","https://openalex.org/W1976076261","https://openalex.org/W1978604688","https://openalex.org/W1988686126","https://openalex.org/W1988790447","https://openalex.org/W2020237802","https://openalex.org/W2031602671","https://openalex.org/W2040884411","https://openalex.org/W2056975304","https://openalex.org/W2078396654","https://openalex.org/W2084457609","https://openalex.org/W2090561883","https://openalex.org/W2093347820","https://openalex.org/W2108002797","https://openalex.org/W2115305054","https://openalex.org/W2121485093","https://openalex.org/W2146081744","https://openalex.org/W4244952642"],"related_works":["https://openalex.org/W2132659060","https://openalex.org/W2031992971","https://openalex.org/W3214791684","https://openalex.org/W2353265673","https://openalex.org/W1529840045","https://openalex.org/W4244036394","https://openalex.org/W2152662039","https://openalex.org/W1842879116","https://openalex.org/W2135107501","https://openalex.org/W2047248895"],"abstract_inverted_index":{"Data":[0],"fusion":[1,50,126,162],"is":[2],"to":[3,88,119],"merge":[4],"the":[5,23,30,38,77,121,156],"results":[6,44,147],"of":[7,25,37,62,79,123,159],"multiple":[8],"independent":[9],"retrieval":[10,31,157],"models":[11,27],"into":[12],"a":[13],"single":[14],"ranked":[15],"list.":[16],"Several":[17],"earlier":[18],"studies":[19],"have":[20,45],"shown":[21],"that":[22,76,91,149],"combination":[24],"different":[26],"can":[28,153],"improve":[29,120,155],"performance":[32,78,122,158],"better":[33],"than":[34],"using":[35],"any":[36],"individual":[39],"models.":[40],"Although":[41],"many":[42],"promising":[43],"been":[46],"given":[47],"by":[48,116],"supervised":[49,124],"methods,":[51],"training":[52,86,94,114],"data":[53,63,115,125,140,161],"sampling":[54,118,151],"has":[55],"attracted":[56],"little":[57],"attention":[58],"in":[59],"previous":[60],"work":[61],"fusion.":[64],"By":[65],"observing":[66],"some":[67],"evaluations":[68],"on":[69,138],"TREC":[70],"and":[71,108,132,144],"NTCIR":[72],"datasets,":[73],"we":[74,102],"found":[75],"one":[80,85],"model":[81],"varied":[82],"largely":[83],"from":[84],"example":[87],"another,":[89],"so":[90],"not":[92],"all":[93],"examples":[95],"were":[96,136],"equivalently":[97],"effective.":[98],"In":[99],"this":[100],"paper,":[101],"propose":[103],"two":[104],"novel":[105],"approaches:":[106],"greedy":[107],"boosting":[109],"approaches,":[110],"which":[111],"select":[112],"effective":[113],"query":[117],"algorithms":[127],"such":[128],"as":[129],"BayesFuse,":[130],"probFuse":[131],"MAPFuse.":[133],"Extensive":[134],"experiments":[135],"conducted":[137],"five":[139],"sets":[141],"including":[142],"TREC-3,4,5":[143],"NTCIR-3,4.":[145],"The":[146],"show":[148],"our":[150],"approaches":[152],"significantly":[154],"those":[160],"methods.":[163]},"counts_by_year":[{"year":2017,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}