{"id":"https://openalex.org/W53630002","doi":null,"title":"Rademacher Complexities and Bounding the Excess Risk in Active Learning","display_name":"Rademacher Complexities and Bounding the Excess Risk in Active Learning","publication_year":2010,"publication_date":"2010-03-01","ids":{"openalex":"https://openalex.org/W53630002","mag":"53630002"},"language":"en","primary_location":{"id":"pmh:oai:CiteSeerX.psu:10.1.1.407.8484","doi":null,"is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.407.8484","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://jmlr.org/papers/volume11/koltchinskii10a/koltchinskii10a.pdf","raw_type":"text"},"volume":null,"type":"article","is_traditional":true,"indexed_in":[],"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/A5029726246","display_name":"Vladimir I. Koltchinskii","orcid":"https://orcid.org/0000-0001-5776-0172","observed_orcids":["https://orcid.org/0000-0001-5776-0172"]},"institutions":[{"id":"https://openalex.org/I130701444","display_name":"Georgia Institute of Technology","ror":"https://ror.org/01zkghx44","country_code":"US","type":"education","lineage":["https://openalex.org/I130701444"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Vladimir Koltchinskii","raw_affiliation_strings":["[Georgia Institute of Technology.]"],"raw_orcid":"https://orcid.org/0000-0001-5776-0172","affiliations":[{"raw_affiliation_string":"[Georgia Institute of Technology.]","institution_ids":["https://openalex.org/I130701444"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5029726246"],"corresponding_institution_ids":["https://openalex.org/I130701444"],"apc_list":null,"apc_paid":null,"fwci":6.0404,"has_fulltext":false,"cited_by_count":65,"citation_normalized_percentile":{"value":0.96159191,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":88,"max":99},"biblio":{"volume":"11","issue":"85","first_page":"2457","last_page":"2485"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12072","display_name":"Machine Learning and Algorithms","score":1.0,"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":1.0,"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/T11612","display_name":"Stochastic Gradient Optimization Techniques","score":0.0,"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/T12101","display_name":"Advanced Bandit Algorithms Research","score":0.0,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/active-learning-machine-learning","display_name":"active learning (machine learning)","score":0.9240000247955322},{"id":"https://openalex.org/keywords/rademacher-complexity","display_name":"Rademacher complexity","score":0.9549999833106995},{"id":"https://openalex.org/keywords/excess-risk","display_name":"excess risk","score":0.8069999814033508},{"id":"https://openalex.org/keywords/sequential-algorithms","display_name":"sequential algorithms","score":0.550000011920929},{"id":"https://openalex.org/keywords/sample-size-calculation","display_name":"sample size determination","score":0.7440000176429749},{"id":"https://openalex.org/keywords/probability-bounds","display_name":"probability bounds","score":0.8809999823570251},{"id":"https://openalex.org/keywords/binary-classification","display_name":"binary classification","score":0.8009999990463257}],"concepts":[{"id":"https://openalex.org/C63584917","wikidata":"https://www.wikidata.org/wiki/Q333286","display_name":"Bounding overwatch","level":2,"score":0.841431736946106},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.7444170713424683},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6953616738319397},{"id":"https://openalex.org/C77967617","wikidata":"https://www.wikidata.org/wiki/Q4677561","display_name":"Active learning (machine learning)","level":2,"score":0.626758873462677},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5595187544822693},{"id":"https://openalex.org/C48372109","wikidata":"https://www.wikidata.org/wiki/Q3913","display_name":"Binary number","level":2,"score":0.5531085729598999},{"id":"https://openalex.org/C66905080","wikidata":"https://www.wikidata.org/wiki/Q17005494","display_name":"Binary classification","level":3,"score":0.48776036500930786},{"id":"https://openalex.org/C176248197","wikidata":"https://www.wikidata.org/wiki/Q458526","display_name":"Probably approximately correct learning","level":4,"score":0.48108431696891785},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.45618051290512085},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.45286768674850464},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.21359720826148987},{"id":"https://openalex.org/C50292564","wikidata":"https://www.wikidata.org/wiki/Q2462783","display_name":"Computational learning theory","level":3,"score":0.1510084867477417},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.058067649602890015},{"id":"https://openalex.org/C94375191","wikidata":"https://www.wikidata.org/wiki/Q11205","display_name":"Arithmetic","level":1,"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/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"pmh:oai:CiteSeerX.psu:10.1.1.407.8484","doi":null,"is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.407.8484","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://jmlr.org/papers/volume11/koltchinskii10a/koltchinskii10a.pdf","raw_type":"text"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.586.1610","doi":null,"is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.586.1610","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://fodava.gatech.edu/files/reports/FODAVA-09-15.pdf","raw_type":"text"},{"id":"mag:53630002","doi":null,"is_oa":false,"landing_page_url":"http://fodava.gatech.edu/files/reports/FODAVA-10-04.pdf","pdf_url":null,"source":{"id":"https://openalex.org/S118988714","display_name":"Journal of Machine Learning Research","issn_l":"1532-4435","issn":["1532-4435","1533-7928"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["anvur-class-a","anvur-scientific","ccf-a","cwts-core","dongbi-a","jufo-3","ki-jl-2","norway-2","poland-140","russia-white-list-1","vabb-shw"],"host_organization":"https://openalex.org/P4310315718","host_organization_name":"The MIT Press","host_organization_lineage":["https://openalex.org/P4310315718","https://openalex.org/P4310316440"],"host_organization_lineage_names":["The MIT Press","Massachusetts Institute of Technology"],"type":"journal"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":"Journal of Machine Learning Research","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W28787979","https://openalex.org/W156419107","https://openalex.org/W648260396","https://openalex.org/W1507545330","https://openalex.org/W1564947197","https://openalex.org/W1600192391","https://openalex.org/W1614597761","https://openalex.org/W2010353172","https://openalex.org/W2029604420","https://openalex.org/W2043919728","https://openalex.org/W2056707879","https://openalex.org/W2059529070","https://openalex.org/W2062896907","https://openalex.org/W2063368770","https://openalex.org/W2076541756","https://openalex.org/W2087258353","https://openalex.org/W2106447856","https://openalex.org/W2117756453","https://openalex.org/W2160828669","https://openalex.org/W2167595980","https://openalex.org/W3022552533","https://openalex.org/W3105849782"],"related_works":["https://openalex.org/W2029604420","https://openalex.org/W7100797290","https://openalex.org/W7096682320","https://openalex.org/W2805772232","https://openalex.org/W2115404432","https://openalex.org/W1533307422","https://openalex.org/W3013722860","https://openalex.org/W7097948147","https://openalex.org/W2583780928","https://openalex.org/W2063368770"],"abstract_inverted_index":{"Sequential":[0],"algorithms":[1,29],"of":[2,9,13,41,52],"active":[3,53],"learning":[4,42],"based":[5],"on":[6,49],"the":[7,10,14,20,28,32,38,50],"estimation":[8],"level":[11],"sets":[12],"empirical":[15],"risk":[16],"are":[17,25,65],"discussed":[18],"in":[19,27,43],"paper.":[21],"Localized":[22],"Rademacher":[23],"complexities":[24],"used":[26],"to":[30,36,61],"estimate":[31],"sample":[33],"sizes":[34],"needed":[35],"achieve":[37],"required":[39],"accuracy":[40],"an":[44],"adaptive":[45],"way.":[46],"Probabilistic":[47],"bounds":[48],"number":[51],"examples":[54],"have":[55],"been":[56],"proved":[57],"and":[58],"several":[59],"applications":[60],"binary":[62],"classification":[63],"problems":[64],"considered.":[66],"1":[67]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":4},{"year":2021,"cited_by_count":5},{"year":2020,"cited_by_count":2},{"year":2019,"cited_by_count":2},{"year":2018,"cited_by_count":2},{"year":2017,"cited_by_count":9},{"year":2016,"cited_by_count":3},{"year":2015,"cited_by_count":7},{"year":2014,"cited_by_count":9},{"year":2013,"cited_by_count":7},{"year":2012,"cited_by_count":4}],"updated_date":"2026-11-04T10:54:05.112534","created_date":"2016-06-24T00:00:00"}
