乐胖代购免代理版

{"id":"https://openalex.org/W2104094955","doi":"https://doi.org/10.1007/s10994-009-5152-4","title":"A theory of learning from different domains","display_name":"A theory of learning from different domains","publication_year":2009,"publication_date":"2009-10-22","ids":{"openalex":"https://openalex.org/W2104094955","doi":"https://doi.org/10.1007/s10994-009-5152-4","mag":"2104094955"},"language":"en","primary_location":{"is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10994-009-5152-4","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10994-009-5152-4.pdf","source":{"id":"https://openalex.org/S62148650","display_name":"Machine Learning","issn_l":"0885-6125","issn":["0885-6125","1573-0565"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319965","https://openalex.org/P4310319900"],"host_organization_lineage_names":["Springer Nature","Springer Science+Business Media"],"type":"journal"},"license":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":"publishedVersion","is_accepted":true,"is_published":true},"type":"article","type_crossref":"journal-article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://link.springer.com/content/pdf/10.1007/s10994-009-5152-4.pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5112193907","display_name":"Shai Ben-David","orcid":null},"institutions":[{"id":"https://openalex.org/I151746483","display_name":"University of Waterloo","ror":"https://ror.org/01aff2v68","country_code":"CA","type":"education","lineage":["https://openalex.org/I151746483"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Shai Ben-David","raw_affiliation_strings":["David R. Cheriton School of Computer Science, University of Waterloo, Waterloo, Canada."],"affiliations":[{"raw_affiliation_string":"David R. Cheriton School of Computer Science, University of Waterloo, Waterloo, Canada.","institution_ids":["https://openalex.org/I151746483"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5053208168","display_name":"John Blitzer","orcid":null},"institutions":[{"id":"https://openalex.org/I95457486","display_name":"University of California, Berkeley","ror":"https://ror.org/01an7q238","country_code":"US","type":"education","lineage":["https://openalex.org/I95457486"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"John Blitzer","raw_affiliation_strings":["Dept. of Computer Science, UC Berkeley, Berkeley, USA"],"affiliations":[{"raw_affiliation_string":"Dept. of Computer Science, UC Berkeley, Berkeley, USA","institution_ids":["https://openalex.org/I95457486"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5006419939","display_name":"Koby Crammer","orcid":"https://orcid.org/0000-0001-8824-5747"},"institutions":[{"id":"https://openalex.org/I174306211","display_name":"Technion \u2013 Israel Institute of Technology","ror":"https://ror.org/03qryx823","country_code":"IL","type":"education","lineage":["https://openalex.org/I174306211"]}],"countries":["IL"],"is_corresponding":false,"raw_author_name":"Koby Crammer","raw_affiliation_strings":["Department of Electrical Engineering, The Technion, Haifa, Israel"],"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, The Technion, Haifa, Israel","institution_ids":["https://openalex.org/I174306211"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5032936063","display_name":"Alex Kulesza","orcid":null},"institutions":[{"id":"https://openalex.org/I79576946","display_name":"University of Pennsylvania","ror":"https://ror.org/00b30xv10","country_code":"US","type":"education","lineage":["https://openalex.org/I79576946"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Alex Kulesza","raw_affiliation_strings":["Department of Computer and Information Science, University of Pennsylvania, Philadelphia, USA"],"affiliations":[{"raw_affiliation_string":"Department of Computer and Information Science, University of Pennsylvania, Philadelphia, USA","institution_ids":["https://openalex.org/I79576946"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5044708805","display_name":"Fernando Pereira","orcid":"https://orcid.org/0000-0001-6100-947X"},"institutions":[{"id":"https://openalex.org/I1291425158","display_name":"Google (United States)","ror":"https://ror.org/00njsd438","country_code":"US","type":"company","lineage":["https://openalex.org/I1291425158","https://openalex.org/I4210128969"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Fernando Pereira","raw_affiliation_strings":["Google Research, Mountain View, USA#TAB#"],"affiliations":[{"raw_affiliation_string":"Google Research, Mountain View, USA#TAB#","institution_ids":["https://openalex.org/I1291425158"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5043117896","display_name":"Jennifer Wortman Vaughan","orcid":"https://orcid.org/0000-0002-7807-2018"},"institutions":[{"id":"https://openalex.org/I136199984","display_name":"Harvard University","ror":"https://ror.org/03vek6s52","country_code":"US","type":"education","lineage":["https://openalex.org/I136199984"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jennifer Wortman Vaughan","raw_affiliation_strings":["School of Engineering & Applied Sciences, Harvard University, Cambridge, USA"],"affiliations":[{"raw_affiliation_string":"School of Engineering & Applied Sciences, Harvard University, Cambridge, USA","institution_ids":["https://openalex.org/I136199984"]}]}],"institution_assertions":[],"countries_distinct_count":3,"institutions_distinct_count":6,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":2390,"currency":"EUR","value_usd":2990,"provenance":"doaj"},"apc_paid":{"value":2390,"currency":"EUR","value_usd":2990,"provenance":"doaj"},"fwci":24.461,"has_fulltext":true,"fulltext_origin":"pdf","cited_by_count":2973,"citation_normalized_percentile":{"value":0.982369,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":99,"max":100},"biblio":{"volume":"79","issue":"1-2","first_page":"151","last_page":"175"},"is_retracted":false,"is_paratext":false,"primary_topic":{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9992,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9992,"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/T12072","display_name":"Machine Learning and Algorithms","score":0.9982,"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":0.9966,"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/discriminative-model","display_name":"Discriminative model","score":0.5630748},{"id":"https://openalex.org/keywords/bounding-overwatch","display_name":"Bounding overwatch","score":0.53066593},{"id":"https://openalex.org/keywords/bayes-error-rate","display_name":"Bayes error rate","score":0.48295945},{"id":"https://openalex.org/keywords/labeled-data","display_name":"Labeled data","score":0.44085124},{"id":"https://openalex.org/keywords/quadratic-classifier","display_name":"Quadratic classifier","score":0.41817892},{"id":"https://openalex.org/keywords/divergence","display_name":"Divergence (linguistics)","score":0.4134515}],"concepts":[{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.80179703},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.66859674},{"id":"https://openalex.org/C183115368","wikidata":"https://www.wikidata.org/wiki/Q856577","display_name":"Weighting","level":2,"score":0.6184407},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.5630748},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5499083},{"id":"https://openalex.org/C63584917","wikidata":"https://www.wikidata.org/wiki/Q333286","display_name":"Bounding overwatch","level":2,"score":0.53066593},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5023401},{"id":"https://openalex.org/C143809311","wikidata":"https://www.wikidata.org/wiki/Q4874458","display_name":"Bayes error rate","level":5,"score":0.48295945},{"id":"https://openalex.org/C2776145971","wikidata":"https://www.wikidata.org/wiki/Q30673951","display_name":"Labeled data","level":2,"score":0.44085124},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4232797},{"id":"https://openalex.org/C16910744","wikidata":"https://www.wikidata.org/wiki/Q7705759","display_name":"Test data","level":2,"score":0.4193732},{"id":"https://openalex.org/C52620605","wikidata":"https://www.wikidata.org/wiki/Q7268357","display_name":"Quadratic classifier","level":3,"score":0.41817892},{"id":"https://openalex.org/C207390915","wikidata":"https://www.wikidata.org/wiki/Q1230525","display_name":"Divergence (linguistics)","level":2,"score":0.4134515},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.39660597},{"id":"https://openalex.org/C185207860","wikidata":"https://www.wikidata.org/wiki/Q17004744","display_name":"Bayes classifier","level":4,"score":0.25460273},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.19258547},{"id":"https://openalex.org/C52001869","wikidata":"https://www.wikidata.org/wiki/Q812530","display_name":"Naive Bayes classifier","level":3,"score":0.13273847},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"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/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C126838900","wikidata":"https://www.wikidata.org/wiki/Q77604","display_name":"Radiology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10994-009-5152-4","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10994-009-5152-4.pdf","source":{"id":"https://openalex.org/S62148650","display_name":"Machine Learning","issn_l":"0885-6125","issn":["0885-6125","1573-0565"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319965","https://openalex.org/P4310319900"],"host_organization_lineage_names":["Springer Nature","Springer Science+Business Media"],"type":"journal"},"license":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":"publishedVersion","is_accepted":true,"is_published":true}],"best_oa_location":{"is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10994-009-5152-4","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10994-009-5152-4.pdf","source":{"id":"https://openalex.org/S62148650","display_name":"Machine Learning","issn_l":"0885-6125","issn":["0885-6125","1573-0565"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319965","https://openalex.org/P4310319900"],"host_organization_lineage_names":["Springer Nature","Springer Science+Business Media"],"type":"journal"},"license":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":"publishedVersion","is_accepted":true,"is_published":true},"sustainable_development_goals":[{"score":0.74,"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities"}],"grants":[],"datasets":[],"versions":[],"referenced_works_count":34,"referenced_works":["https://openalex.org/W1542886316","https://openalex.org/W1607832978","https://openalex.org/W1608944489","https://openalex.org/W1773803948","https://openalex.org/W1853837125","https://openalex.org/W1966026565","https://openalex.org/W1967807490","https://openalex.org/W1968934255","https://openalex.org/W2062291443","https://openalex.org/W2091825929","https://openalex.org/W2092654472","https://openalex.org/W2104936489","https://openalex.org/W2105523772","https://openalex.org/W2110091014","https://openalex.org/W2111362445","https://openalex.org/W2112483442","https://openalex.org/W2120354757","https://openalex.org/W2120587290","https://openalex.org/W2122838776","https://openalex.org/W2130903752","https://openalex.org/W2131953535","https://openalex.org/W2134169350","https://openalex.org/W2139122730","https://openalex.org/W2148440006","https://openalex.org/W2148603752","https://openalex.org/W2162888803","https://openalex.org/W2163302275","https://openalex.org/W2163918411","https://openalex.org/W2166706824","https://openalex.org/W22024230","https://openalex.org/W2579923771","https://openalex.org/W3104240813","https://openalex.org/W3146306708","https://openalex.org/W4249716558"],"related_works":["https://openalex.org/W92531827","https://openalex.org/W58702947","https://openalex.org/W4287241967","https://openalex.org/W3144173820","https://openalex.org/W2187639235","https://openalex.org/W2115065944","https://openalex.org/W2060931694","https://openalex.org/W1849414697","https://openalex.org/W1801413419","https://openalex.org/W1622451593"],"abstract_inverted_index":{"Discriminative":[0],"learning":[1],"methods":[2],"for":[3],"classification":[4],"perform":[5,75],"well":[6,38,76,164],"when":[7],"training":[8,24,52,95],"and":[9,47,130,209,244,259,274,297],"test":[10,111],"data":[11,25,71,103],"are":[12],"drawn":[13],"from":[14,26,69,147,151,229],"the":[15,97,106,115,131,134,152,155,175,180,189,194,206,219,223,230,239,251,253,260,263,270,288],"same":[16],"distribution.":[17],"Often,":[18],"however,":[19],"we":[20,57,91,168],"have":[21],"plentiful":[22],"labeled":[23,51,86,101],"a":[27,34,40,44,66,82,120,139,184,198,202,248,282],"source":[28,70,102,128,177,208,220,243,296],"domain":[29,42],"but":[30],"wish":[31],"to":[32,74,104,237],"learn":[33],"classifier":[35,67],"which":[36,200,284],"performs":[37,163],"on":[39,77],"target":[41,78,87,108,122,181,195,210,224,245,289,298],"with":[43,96,174],"different":[45],"distribution":[46],"little":[48],"or":[49,226,291],"no":[50],"data.":[53],"In":[54],"this":[55,171],"work":[56,214],"investigate":[58],"two":[59,135,231],"questions.":[60],"First,":[61],"under":[62],"what":[63],"conditions":[64],"can":[65,144],"trained":[68],"be":[72,145],"expected":[73],"data?":[79],"Second,":[80],"given":[81],"small":[83],"amount":[84,99],"of":[85,100,126,183,197,205,242,250,256,262,295],"data,":[88],"how":[89,236],"should":[90],"combine":[92],"it":[93],"during":[94],"large":[98],"achieve":[105],"lowest":[107],"error":[109,123,129,178,182,196,246,290],"at":[110,277],"time?":[112],"We":[113,137,187,234],"address":[114],"first":[116],"question":[117,191],"by":[118,192],"bounding":[119,193],"classifier's":[121],"in":[124,165],"terms":[125],"its":[127],"divergence":[132,141],"between":[133],"domains.":[136,153],"give":[138],"classifier-induced":[140],"measure":[142],"that":[143,157,162,170],"estimated":[146],"finite,":[148],"unlabeled":[149],"samples":[150],"Under":[154],"assumption":[156],"there":[158],"exists":[159],"some":[160],"hypothesis":[161,264],"both":[166,257],"domains,":[167,258],"show":[169,235],"quantity":[172],"together":[173],"empirical":[176,207],"characterize":[179],"source-trained":[185],"classifier.":[186],"answer":[188],"second":[190],"model":[199],"minimizes":[201],"convex":[203],"combination":[204,241],"errors.":[211,299],"Previous":[212],"theoretical":[213],"has":[215],"considered":[216],"minimizing":[217,286],"just":[218,222],"error,":[221,225],"weighting":[227,294],"instances":[228],"domains":[232],"equally.":[233],"choose":[238],"optimal":[240],"as":[247,279,281],"function":[249],"divergence,":[252],"sample":[254],"sizes":[255],"complexity":[261],"class.":[265],"The":[266],"resulting":[267],"bound":[268,283],"generalizes":[269],"previously":[271],"studied":[272],"cases":[273],"is":[275],"always":[276],"least":[278],"tight":[280],"considers":[285],"only":[287],"an":[292],"equal":[293]},"cited_by_api_url":"https://api.openalex.org/works?filter=cites:W2104094955","counts_by_year":[{"year":2024,"cited_by_count":316},{"year":2023,"cited_by_count":406},{"year":2022,"cited_by_count":365},{"year":2021,"cited_by_count":570},{"year":2020,"cited_by_count":498},{"year":2019,"cited_by_count":306},{"year":2018,"cited_by_count":146},{"year":2017,"cited_by_count":96},{"year":2016,"cited_by_count":70},{"year":2015,"cited_by_count":43},{"year":2014,"cited_by_count":43},{"year":2013,"cited_by_count":44},{"year":2012,"cited_by_count":32}],"updated_date":"2025-01-16T03:00:08.617567","created_date":"2016-06-24"}