{"id":"https://openalex.org/W2610690067","doi":"https://doi.org/10.1145/1390156.1390233","title":"Random classification noise defeats all convex potential boosters","display_name":"Random classification noise defeats all convex potential boosters","publication_year":2008,"publication_date":"2008-01-01","ids":{"openalex":"https://openalex.org/W2610690067","doi":"https://doi.org/10.1145/1390156.1390233","mag":"2610690067"},"language":"en","primary_location":{"id":"doi:10.1145/1390156.1390233","is_oa":false,"landing_page_url":"https://doi.org/10.1145/1390156.1390233","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 25th international conference on Machine learning - ICML '08","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/A5084487318","display_name":"Philip M. Long","orcid":"https://orcid.org/0000-0002-1010-6197"},"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":true,"raw_author_name":"Philip M. Long","raw_affiliation_strings":["Google, Mountain View, CA"],"affiliations":[{"raw_affiliation_string":"Google, Mountain View, CA","institution_ids":["https://openalex.org/I1291425158"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5014866889","display_name":"Rocco A. Servedio","orcid":"https://orcid.org/0000-0003-2407-543X"},"institutions":[{"id":"https://openalex.org/I78577930","display_name":"Columbia University","ror":"https://ror.org/00hj8s172","country_code":"US","type":"education","lineage":["https://openalex.org/I78577930"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Rocco A. Servedio","raw_affiliation_strings":["Columbia University, New York, NY"],"affiliations":[{"raw_affiliation_string":"Columbia University, New York, NY","institution_ids":["https://openalex.org/I78577930"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5084487318"],"corresponding_institution_ids":["https://openalex.org/I1291425158"],"apc_list":null,"apc_paid":null,"fwci":7.4367,"has_fulltext":false,"cited_by_count":42,"citation_normalized_percentile":{"value":0.97011566,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"608","last_page":"615"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12072","display_name":"Machine Learning and Algorithms","score":0.9983999729156494,"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":0.9983999729156494,"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":0.9919999837875366,"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":0.9757999777793884,"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/boosting","display_name":"Boosting (machine learning)","score":0.5798567533493042},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5679845809936523},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5170705914497375},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.5040522813796997},{"id":"https://openalex.org/keywords/noise-measurement","display_name":"Noise measurement","score":0.4759785234928131},{"id":"https://openalex.org/keywords/adaboost","display_name":"AdaBoost","score":0.47417986392974854},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.46563827991485596},{"id":"https://openalex.org/keywords/convex-function","display_name":"Convex function","score":0.4610345661640167},{"id":"https://openalex.org/keywords/stochastic-gradient-descent","display_name":"Stochastic gradient descent","score":0.4587552547454834},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.44124385714530945},{"id":"https://openalex.org/keywords/regular-polygon","display_name":"Regular polygon","score":0.4041852056980133},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.36707836389541626},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.20770129561424255},{"id":"https://openalex.org/keywords/noise-reduction","display_name":"Noise reduction","score":0.19657427072525024},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.09269735217094421}],"concepts":[{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.5798567533493042},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5679845809936523},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5170705914497375},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.5040522813796997},{"id":"https://openalex.org/C29265498","wikidata":"https://www.wikidata.org/wiki/Q7047719","display_name":"Noise measurement","level":3,"score":0.4759785234928131},{"id":"https://openalex.org/C141404830","wikidata":"https://www.wikidata.org/wiki/Q2823869","display_name":"AdaBoost","level":3,"score":0.47417986392974854},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.46563827991485596},{"id":"https://openalex.org/C145446738","wikidata":"https://www.wikidata.org/wiki/Q319913","display_name":"Convex function","level":3,"score":0.4610345661640167},{"id":"https://openalex.org/C206688291","wikidata":"https://www.wikidata.org/wiki/Q7617819","display_name":"Stochastic gradient descent","level":3,"score":0.4587552547454834},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.44124385714530945},{"id":"https://openalex.org/C112680207","wikidata":"https://www.wikidata.org/wiki/Q714886","display_name":"Regular polygon","level":2,"score":0.4041852056980133},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.36707836389541626},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.20770129561424255},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.19657427072525024},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.09269735217094421},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/1390156.1390233","is_oa":false,"landing_page_url":"https://doi.org/10.1145/1390156.1390233","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 25th international conference on Machine learning - ICML '08","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":27,"referenced_works":["https://openalex.org/W1540007258","https://openalex.org/W1562197959","https://openalex.org/W1570592793","https://openalex.org/W1596216468","https://openalex.org/W1605688901","https://openalex.org/W1850527962","https://openalex.org/W1931284523","https://openalex.org/W1988790447","https://openalex.org/W2023163512","https://openalex.org/W2024046085","https://openalex.org/W2032210760","https://openalex.org/W2035446310","https://openalex.org/W2068221105","https://openalex.org/W2070534370","https://openalex.org/W2098933520","https://openalex.org/W2108263314","https://openalex.org/W2112076978","https://openalex.org/W2115140232","https://openalex.org/W2128808713","https://openalex.org/W2151097972","https://openalex.org/W2154436219","https://openalex.org/W2157112198","https://openalex.org/W2175482000","https://openalex.org/W2294105907","https://openalex.org/W2912953292","https://openalex.org/W3098897816","https://openalex.org/W4285719527"],"related_works":["https://openalex.org/W2327035729","https://openalex.org/W2348748958","https://openalex.org/W3039673966","https://openalex.org/W1538046993","https://openalex.org/W2884325279","https://openalex.org/W1570592793","https://openalex.org/W1525436954","https://openalex.org/W2385662756","https://openalex.org/W2585372724","https://openalex.org/W2241444561"],"abstract_inverted_index":{"A":[0],"broad":[1,46],"class":[2,27,47],"of":[3,19,22,48,87,154],"boosting":[4,54],"algorithms":[5],"can":[6,145],"be":[7,105],"interpreted":[8],"as":[9],"performing":[10],"coordinate-wise":[11],"gradient":[12],"descent":[13],"to":[14,59,107,148],"minimize":[15],"some":[16],"potential":[17,50,140],"function":[18,141],"the":[20,138,152],"margins":[21],"a":[23,45,83,95],"data":[24,85],"set.":[25],"This":[26,121],"includes":[28],"AdaBoost,":[29],"LogitBoost,":[30],"and":[31,35,73,143],"other":[32],"widely":[33],"used":[34],"well-studied":[36],"boosters.":[37],"In":[38],"this":[39,65],"paper":[40],"we":[41],"show":[42],"that":[43,68],"for":[44,69],"convex":[49,139],"functions,":[51],"any":[52,70,74],"such":[53,71,94],"algorithm":[55],"is":[56,82,90,99,114,124],"highly":[57],"susceptible":[58],"random":[60,76,115,155],"classification":[61,77,116,156],"noise.":[62,157],"We":[63],"do":[64,134],"by":[66,93],"showing":[67],"booster":[72,96],"nonzero":[75],"noise":[78,117],"rate":[79,119],"\u03b7,":[80],"there":[81,98,113],"simple":[84],"set":[86],"examples":[88],"which":[89,103,133,144],"efficiently":[91],"learnable":[92],"if":[97,112],"no":[100],"noise,":[101],"but":[102],"cannot":[104],"learned":[106],"accuracy":[108,150],"better":[109],"than":[110],"1/2":[111],"at":[118],"\u03b7.":[120],"negative":[122],"result":[123],"in":[125,151],"contrast":[126],"with":[127],"known":[128],"branching":[129],"program":[130],"based":[131],"boosters":[132],"not":[135],"fall":[136],"into":[137],"framework":[142],"provably":[146],"learn":[147],"high":[149],"presence":[153]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":1},{"year":2020,"cited_by_count":2},{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":2},{"year":2017,"cited_by_count":2},{"year":2016,"cited_by_count":1},{"year":2015,"cited_by_count":4},{"year":2014,"cited_by_count":2},{"year":2013,"cited_by_count":2},{"year":2012,"cited_by_count":7}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}