{"id":"https://openalex.org/W4313067145","doi":"https://doi.org/10.1109/ijcnn55064.2022.9892204","title":"An Empirical Analysis of Boosting Deep Networks","display_name":"An Empirical Analysis of Boosting Deep Networks","publication_year":2022,"publication_date":"2022-07-18","ids":{"openalex":"https://openalex.org/W4313067145","doi":"https://doi.org/10.1109/ijcnn55064.2022.9892204"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn55064.2022.9892204","doi":"https://doi.org/10.1109/ijcnn55064.2022.9892204","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn55064.2022.9892204","pdf_url":null,"source":{"id":"https://openalex.org/S4363607707","display_name":"International Joint Conference on Neural Networks (IJCNN)","issn_l":"2161-4407","issn":["2161-4407"],"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":["jufo-1","norway-1"],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"},"volume":null,"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/A5088236196","display_name":"Sai Saketh Rambhatla","orcid":"https://orcid.org/0009-0002-2135-8311","observed_orcids":["https://orcid.org/0009-0002-2135-8311"]},"institutions":[{"id":"https://openalex.org/I66946132","display_name":"University of Maryland, College Park","ror":"https://ror.org/047s2c258","country_code":"US","type":"education","lineage":["https://openalex.org/I66946132"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Sai Saketh Rambhatla","raw_affiliation_strings":["Electrical and Computer Engineering University of Maryland, College Park, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Electrical and Computer Engineering University of Maryland, College Park, USA","institution_ids":["https://openalex.org/I66946132"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5064275902","display_name":"Michael Jones","orcid":"https://orcid.org/0000-0001-5215-2346","observed_orcids":["https://orcid.org/0000-0001-5215-2346"]},"institutions":[{"id":"https://openalex.org/I4405253225","display_name":"Mitsubishi Electric Research Laboratories (United States)","ror":"https://ror.org/00e2e3n51","country_code":"US","type":"company","lineage":["https://openalex.org/I1306287861","https://openalex.org/I4405253225"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Michael J. Jones","raw_affiliation_strings":["Mitsubishi Electric Research Laboratories, Cambridge, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Mitsubishi Electric Research Laboratories, Cambridge, USA","institution_ids":["https://openalex.org/I4405253225"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5102762707","display_name":"Rama Chellappa","orcid":"https://orcid.org/0000-0002-7638-1650","observed_orcids":["https://orcid.org/0000-0002-7638-1650"]},"institutions":[{"id":"https://openalex.org/I145311948","display_name":"Johns Hopkins University","ror":"https://ror.org/00za53h95","country_code":"US","type":"education","lineage":["https://openalex.org/I145311948"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Rama Chellappa","raw_affiliation_strings":["Electrical and Computer Engineering Johns Hopkins University, Baltimore, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Electrical and Computer Engineering Johns Hopkins University, Baltimore, USA","institution_ids":["https://openalex.org/I145311948"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.14856551,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"2","issue":null,"first_page":"1","last_page":"9"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12535","display_name":"Machine Learning and Data Classification","score":0.4406999945640564,"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":0.4406999945640564,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.3474000096321106,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.08240000158548355,"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","score":0.9959999918937683},{"id":"https://openalex.org/keywords/ensemble-learning","display_name":"ensemble learning","score":0.7599999904632568},{"id":"https://openalex.org/keywords/deep-neural-network","display_name":"deep neural network","score":0.8510000109672546},{"id":"https://openalex.org/keywords/generalization","display_name":"generalization","score":0.8339999914169312},{"id":"https://openalex.org/keywords/convolutional-neural-networks","display_name":"convolutional neural networks","score":0.8149999976158142},{"id":"https://openalex.org/keywords/multilayer-perceptron","display_name":"multilayer perceptron","score":1.0},{"id":"https://openalex.org/keywords/decision-tree","display_name":"decision tree","score":0.8190000057220459},{"id":"https://openalex.org/keywords/object-recognition","display_name":"object recognition","score":0.8679999709129333},{"id":"https://openalex.org/keywords/weak-classifiers","display_name":"weak classifiers","score":0.4350000023841858}],"concepts":[{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.780511736869812},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6197463870048523},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4178786873817444}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn55064.2022.9892204","doi":"https://doi.org/10.1109/ijcnn55064.2022.9892204","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn55064.2022.9892204","pdf_url":null,"source":{"id":"https://openalex.org/S4363607707","display_name":"International Joint Conference on Neural Networks (IJCNN)","issn_l":"2161-4407","issn":["2161-4407"],"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":["jufo-1","norway-1"],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":34,"referenced_works":["https://openalex.org/W1481643961","https://openalex.org/W1594031697","https://openalex.org/W1678356000","https://openalex.org/W1686810756","https://openalex.org/W1968969471","https://openalex.org/W1988790447","https://openalex.org/W2024046085","https://openalex.org/W2070534370","https://openalex.org/W2093717447","https://openalex.org/W2108263314","https://openalex.org/W2112076978","https://openalex.org/W2112796928","https://openalex.org/W2117085409","https://openalex.org/W2119113516","https://openalex.org/W2134987394","https://openalex.org/W2164921999","https://openalex.org/W2168020168","https://openalex.org/W2172734211","https://openalex.org/W2295598076","https://openalex.org/W2335728318","https://openalex.org/W2550814094","https://openalex.org/W2566079294","https://openalex.org/W2612354813","https://openalex.org/W2613763509","https://openalex.org/W2741883069","https://openalex.org/W2782476368","https://openalex.org/W2898227265","https://openalex.org/W2899748887","https://openalex.org/W2963518130","https://openalex.org/W2967733078","https://openalex.org/W3097096317","https://openalex.org/W3118608800","https://openalex.org/W4244952642","https://openalex.org/W6703116779"],"related_works":["https://openalex.org/W3183886660","https://openalex.org/W1481639454","https://openalex.org/W2114298973","https://openalex.org/W2100805904","https://openalex.org/W1850527962","https://openalex.org/W1492933470","https://openalex.org/W2164921999","https://openalex.org/W2613763509","https://openalex.org/W4282016089","https://openalex.org/W2941628744"],"abstract_inverted_index":{"Boosting":[0],"is":[1,26,150,157],"a":[2,6,27,69,83,126,134],"method":[3,28],"for":[4,29,44],"finding":[5],"highly":[7],"accurate":[8],"classifier":[9],"by":[10],"linearly":[11],"combining":[12],"many":[13],"\u201cweak\u201d":[14],"classifiers,":[15],"each":[16],"of":[17,33,72,91,137,146,163],"which":[18],"may":[19],"be":[20,41,108],"only":[21],"moderately":[22],"accurate.":[23],"Thus,":[24],"boosting":[25,36,100,164],"learning":[30],"an":[31,151],"ensemble":[32,71,136],"classifiers.":[34,168],"While":[35,148],"has":[37,52],"been":[38,54],"shown":[39],"to":[40,107,159],"very":[42],"effective":[43],"decision":[45,73,85],"trees,":[46],"its":[47],"impact":[48],"on":[49,79],"neural":[50,112,116,128,139],"networks":[51,113,117,140],"not":[53],"extensively":[55],"studied.":[56],"Using":[57],"standard":[58],"object":[59],"recognition":[60],"datasets,":[61],"we":[62],"verify":[63],"experimentally":[64],"the":[65,88,96,105,142,161],"well-known":[66],"result":[67],"that":[68,125],"boosted":[70,135],"trees":[74],"usually":[75,130],"generalizes":[76,131],"much":[77],"better":[78,132],"testing":[80],"data":[81],"than":[82,133],"single":[84,127],"tree":[86],"with":[87,141],"same":[89,97,143],"number":[90,145],"parameters.":[92,147],"In":[93],"contrast,":[94],"using":[95,111],"datasets":[98],"and":[99,119],"algorithms,":[101],"our":[102],"experiments":[103],"show":[104],"opposite":[106],"true":[109],"when":[110],"(both":[114],"convolutional":[115],"(CNNs)":[118],"multilayer":[120],"perceptrons":[121],"(MLPs)).":[122],"We":[123],"find":[124],"network":[129],"smaller":[138],"total":[144],"this":[149],"experimental":[152],"investigation,":[153],"more":[154],"theoretical":[155],"research":[156],"warranted":[158],"understand":[160],"role":[162],"in":[165],"deep":[166],"learning-based":[167]},"counts_by_year":[],"updated_date":"2026-11-04T10:54:05.112534","created_date":"2023-01-06T00:00:00"}