{"id":"https://openalex.org/W2609611988","doi":"https://doi.org/10.1109/icpr.2016.7899632","title":"Regularizing AdaBoost with validation sets of increasing size","display_name":"Regularizing AdaBoost with validation sets of increasing size","publication_year":2016,"publication_date":"2016-12-01","ids":{"openalex":"https://openalex.org/W2609611988","doi":"https://doi.org/10.1109/icpr.2016.7899632","mag":"2609611988"},"language":"en","primary_location":{"id":"doi:10.1109/icpr.2016.7899632","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icpr.2016.7899632","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 23rd International Conference on Pattern Recognition (ICPR)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5110465143","display_name":"D. W. Meijer","orcid":null},"institutions":[{"id":"https://openalex.org/I98358874","display_name":"Delft University of Technology","ror":"https://ror.org/02e2c7k09","country_code":"NL","type":"education","lineage":["https://openalex.org/I98358874"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Dirk W.J. Meijer","raw_affiliation_strings":["Delft University of Technology, Delft, The Netherlands"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Delft University of Technology, Delft, The Netherlands","institution_ids":["https://openalex.org/I98358874"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5001295629","display_name":"David M. J. Tax","orcid":"https://orcid.org/0000-0002-5153-9087"},"institutions":[{"id":"https://openalex.org/I98358874","display_name":"Delft University of Technology","ror":"https://ror.org/02e2c7k09","country_code":"NL","type":"education","lineage":["https://openalex.org/I98358874"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"David M.J. Tax","raw_affiliation_strings":["Delft University of Technology, Delft, The Netherlands"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Delft University of Technology, Delft, The Netherlands","institution_ids":["https://openalex.org/I98358874"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I98358874"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":"9","issue":null,"first_page":"192","last_page":"197"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12535","display_name":"Machine Learning and Data Classification","score":0.9987999796867371,"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/T12535","display_name":"Machine Learning and Data Classification","score":0.9987999796867371,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9979000091552734,"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/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.9836999773979187,"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/overfitting","display_name":"Overfitting","score":0.9390081763267517},{"id":"https://openalex.org/keywords/adaboost","display_name":"AdaBoost","score":0.8846126794815063},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7513684034347534},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7421537637710571},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.7114126682281494},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.6663926839828491},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.4983377456665039},{"id":"https://openalex.org/keywords/regularization","display_name":"Regularization (linguistics)","score":0.47436317801475525},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.43917131423950195},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.1106671392917633}],"concepts":[{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.9390081763267517},{"id":"https://openalex.org/C141404830","wikidata":"https://www.wikidata.org/wiki/Q2823869","display_name":"AdaBoost","level":3,"score":0.8846126794815063},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7513684034347534},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7421537637710571},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.7114126682281494},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.6663926839828491},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.4983377456665039},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.47436317801475525},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.43917131423950195},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.1106671392917633}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icpr.2016.7899632","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icpr.2016.7899632","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 23rd International Conference on Pattern Recognition (ICPR)","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":17,"referenced_works":["https://openalex.org/W1521548765","https://openalex.org/W1561625256","https://openalex.org/W1661786820","https://openalex.org/W1968969471","https://openalex.org/W1988790447","https://openalex.org/W2024046085","https://openalex.org/W2099968818","https://openalex.org/W2122168905","https://openalex.org/W2124104513","https://openalex.org/W2490901831","https://openalex.org/W3120740533","https://openalex.org/W4285719527","https://openalex.org/W6631264160","https://openalex.org/W6637232609","https://openalex.org/W6675272622","https://openalex.org/W6677809728","https://openalex.org/W6678507914"],"related_works":["https://openalex.org/W2376566779","https://openalex.org/W2166180245","https://openalex.org/W2052828961","https://openalex.org/W1521548765","https://openalex.org/W4388103534","https://openalex.org/W4318142952","https://openalex.org/W4312531262","https://openalex.org/W2972862903","https://openalex.org/W2072070946","https://openalex.org/W2981877337"],"abstract_inverted_index":{"AdaBoost":[0,26,79,98,121],"is":[1],"an":[2],"iterative":[3],"algorithm":[4],"to":[5,21,28,32,97,118],"construct":[6],"classifier":[7,71],"ensembles.":[8],"It":[9],"quickly":[10],"achieves":[11,94],"high":[12],"accuracy":[13],"by":[14],"focusing":[15],"on":[16,99,105],"objects":[17],"that":[18,37,80],"are":[19],"difficult":[20],"classify.":[22],"Because":[23],"of":[24,46,68,78],"this,":[25],"tends":[27],"overfit":[29,119],"when":[30,120],"subjected":[31],"noisy":[33,53,106],"datasets.":[34],"We":[35,73],"observe":[36],"this":[38],"can":[39],"be":[40],"partially":[41],"prevented":[42],"with":[43,90],"the":[44,51,60,66,69,85],"use":[45],"validation":[47,82],"sets,":[48],"taken":[49],"from":[50,84],"same":[52],"training":[54,64],"set.":[55],"But":[56],"using":[57],"less":[58],"than":[59],"full":[61],"dataset":[62],"for":[63],"hurts":[65],"performance":[67,95,104],"final":[70],"ensemble.":[72],"introduce":[74],"ValidBoost,":[75],"a":[76],"regularization":[77],"takes":[81],"sets":[83],"dataset,":[86],"increasing":[87],"in":[88],"size":[89],"each":[91],"iteration.":[92],"ValidBoost":[93],"similar":[96,111],"noise-free":[100],"datasets":[101],"and":[102],"improved":[103],"datasets,":[107],"as":[108],"it":[109],"performs":[110],"at":[112],"first,":[113],"but":[114],"does":[115],"not":[116],"start":[117],"does.":[122]},"counts_by_year":[{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
