{"id":"https://openalex.org/W3091267322","doi":"https://doi.org/10.1109/ijcnn48605.2020.9207124","title":"Dense Broad Learning System based on Conjugate Gradient","display_name":"Dense Broad Learning System based on Conjugate Gradient","publication_year":2020,"publication_date":"2020-07-01","ids":{"openalex":"https://openalex.org/W3091267322","doi":"https://doi.org/10.1109/ijcnn48605.2020.9207124","mag":"3091267322"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn48605.2020.9207124","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn48605.2020.9207124","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 International Joint Conference on Neural Networks (IJCNN)","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/A5015096297","display_name":"Weidong Zou","orcid":"https://orcid.org/0000-0002-2227-7524"},"institutions":[{"id":"https://openalex.org/I125839683","display_name":"Beijing Institute of Technology","ror":"https://ror.org/01skt4w74","country_code":"CN","type":"education","lineage":["https://openalex.org/I125839683","https://openalex.org/I890469752"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Weidong Zou","raw_affiliation_strings":["School of Automation, Beijing Institute of Technology, Beijing, China"],"affiliations":[{"raw_affiliation_string":"School of Automation, Beijing Institute of Technology, Beijing, China","institution_ids":["https://openalex.org/I125839683"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5064231378","display_name":"Yuanqing Xia","orcid":"https://orcid.org/0000-0002-5977-4911"},"institutions":[{"id":"https://openalex.org/I125839683","display_name":"Beijing Institute of Technology","ror":"https://ror.org/01skt4w74","country_code":"CN","type":"education","lineage":["https://openalex.org/I125839683","https://openalex.org/I890469752"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuanqing Xia","raw_affiliation_strings":["School of Automation, Beijing Institute of Technology, Beijing, China"],"affiliations":[{"raw_affiliation_string":"School of Automation, Beijing Institute of Technology, Beijing, China","institution_ids":["https://openalex.org/I125839683"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5028260055","display_name":"Weipeng Cao","orcid":"https://orcid.org/0000-0003-2414-6066"},"institutions":[{"id":"https://openalex.org/I180726961","display_name":"Shenzhen University","ror":"https://ror.org/01vy4gh70","country_code":"CN","type":"education","lineage":["https://openalex.org/I180726961"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Weipeng Cao","raw_affiliation_strings":["College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China"],"affiliations":[{"raw_affiliation_string":"College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China","institution_ids":["https://openalex.org/I180726961"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5015096297"],"corresponding_institution_ids":["https://openalex.org/I125839683"],"apc_list":null,"apc_paid":null,"fwci":0.5493,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.73242551,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12676","display_name":"Machine Learning and ELM","score":0.9998999834060669,"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/T12676","display_name":"Machine Learning and ELM","score":0.9998999834060669,"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":0.9868000149726868,"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/T14225","display_name":"Advanced Sensor and Control Systems","score":0.9843000173568726,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/gradient-descent","display_name":"Gradient descent","score":0.7545522451400757},{"id":"https://openalex.org/keywords/conjugate-gradient-method","display_name":"Conjugate gradient method","score":0.7029529809951782},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.657785952091217},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6449650526046753},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.575799286365509},{"id":"https://openalex.org/keywords/node","display_name":"Node (physics)","score":0.5633716583251953},{"id":"https://openalex.org/keywords/convergence","display_name":"Convergence (economics)","score":0.5562044382095337},{"id":"https://openalex.org/keywords/stability","display_name":"Stability (learning theory)","score":0.5264694690704346},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.46606308221817017},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.45547178387641907},{"id":"https://openalex.org/keywords/ridge","display_name":"Ridge","score":0.43410632014274597},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4222533702850342},{"id":"https://openalex.org/keywords/nonlinear-conjugate-gradient-method","display_name":"Nonlinear conjugate gradient method","score":0.4104929566383362},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.3408423066139221},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.31158947944641113},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.3033870458602905},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.20918461680412292}],"concepts":[{"id":"https://openalex.org/C153258448","wikidata":"https://www.wikidata.org/wiki/Q1199743","display_name":"Gradient descent","level":3,"score":0.7545522451400757},{"id":"https://openalex.org/C81184566","wikidata":"https://www.wikidata.org/wiki/Q1191895","display_name":"Conjugate gradient method","level":2,"score":0.7029529809951782},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.657785952091217},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6449650526046753},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.575799286365509},{"id":"https://openalex.org/C62611344","wikidata":"https://www.wikidata.org/wiki/Q1062658","display_name":"Node (physics)","level":2,"score":0.5633716583251953},{"id":"https://openalex.org/C2777303404","wikidata":"https://www.wikidata.org/wiki/Q759757","display_name":"Convergence (economics)","level":2,"score":0.5562044382095337},{"id":"https://openalex.org/C112972136","wikidata":"https://www.wikidata.org/wiki/Q7595718","display_name":"Stability (learning theory)","level":2,"score":0.5264694690704346},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.46606308221817017},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.45547178387641907},{"id":"https://openalex.org/C32277403","wikidata":"https://www.wikidata.org/wiki/Q740445","display_name":"Ridge","level":2,"score":0.43410632014274597},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4222533702850342},{"id":"https://openalex.org/C26362088","wikidata":"https://www.wikidata.org/wiki/Q17086453","display_name":"Nonlinear conjugate gradient method","level":4,"score":0.4104929566383362},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.3408423066139221},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.31158947944641113},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3033870458602905},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.20918461680412292},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"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/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"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/C50522688","wikidata":"https://www.wikidata.org/wiki/Q189833","display_name":"Economic growth","level":1,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C66938386","wikidata":"https://www.wikidata.org/wiki/Q633538","display_name":"Structural engineering","level":1,"score":0.0},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn48605.2020.9207124","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn48605.2020.9207124","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W1996640396","https://openalex.org/W2012231377","https://openalex.org/W2729405303","https://openalex.org/W2738226240","https://openalex.org/W2753648062","https://openalex.org/W2791759164","https://openalex.org/W2809352434","https://openalex.org/W2885019847","https://openalex.org/W2887576641","https://openalex.org/W2889731581","https://openalex.org/W2890126432","https://openalex.org/W2890317121","https://openalex.org/W2890706287","https://openalex.org/W2893639361","https://openalex.org/W2899329799","https://openalex.org/W2909676215","https://openalex.org/W2910673030","https://openalex.org/W2923793675","https://openalex.org/W2978443331","https://openalex.org/W2978843887","https://openalex.org/W2995117712","https://openalex.org/W6753662560","https://openalex.org/W6755025083"],"related_works":["https://openalex.org/W2098528027","https://openalex.org/W3197592642","https://openalex.org/W2625259661","https://openalex.org/W2388344948","https://openalex.org/W2069731254","https://openalex.org/W2005717169","https://openalex.org/W2393037300","https://openalex.org/W2356977358","https://openalex.org/W2045319544","https://openalex.org/W3159907817"],"abstract_inverted_index":{"Conventional":[0],"training":[1,68],"mechanism":[2,38],"for":[3,115],"deep":[4,31],"learning,":[5],"which":[6,128,162],"is":[7,96,122,166,176],"based":[8,153],"on":[9,154,212],"gradient":[10,193],"descent,":[11],"suffers":[12],"from":[13,217],"many":[14,87],"notorious":[15],"issues":[16],"such":[17],"as":[18],"low":[19],"convergence":[20],"rate,":[21],"over-fitting,":[22],"and":[23,63,70,136,172,191,206,228,234],"time-consuming.":[24],"To":[25,143],"alleviate":[26],"these":[27],"problems,":[28],"a":[29,35,97,149,183],"novel":[30,150],"learning":[32,37],"algorithm":[33],"with":[34],"different":[36],"named":[39],"Broad":[40],"Learning":[41],"System":[42],"(BLS)":[43],"was":[44],"proposed":[45,148],"by":[46],"Prof.":[47],"C.":[48],"L.":[49],"Philip":[50],"Chen":[51],"in":[52,86,158,161,182],"2017.":[53],"BLS":[54,81,121,152,233],"randomly":[55],"selects":[56],"the":[57,60,72,108,112,116,132,139,199,203],"parameters":[58],"of":[59,90,111,120,134,141,202],"feature":[61,164,170,204],"nodes":[62,65,171,181,205,208],"enhancement":[64,174,180,207],"during":[66],"its":[67,78,91,235],"process":[69],"uses":[71],"ridge":[73,117],"regression":[74,118,215],"theory":[75],"to":[76,124,131,168,178,197],"solve":[77,144],"output":[79,200],"weights.":[80],"has":[82,101],"been":[83,104],"widely":[84],"used":[85,196],"fields":[88],"because":[89],"high":[92],"efficiency.":[93],"However,":[94],"there":[95],"fundamental":[98],"problem":[99,133],"that":[100,106,221],"not":[102],"yet":[103],"solved,":[105],"is,":[107],"appropriate":[109],"value":[110],"parameter":[113],"\u03bb":[114],"operation":[119],"difficult":[123],"be":[125],"set":[126],"properly,":[127],"often":[129],"leads":[130],"over-fitting":[135],"seriously":[137],"limits":[138],"development":[140],"BLS.":[142],"this":[145,159],"problem,":[146],"we":[147],"Dense":[151],"Conjugate":[155],"Gradient":[156],"(CG-DBLS)":[157],"paper,":[160],"each":[163,173],"node":[165,175],"connected":[167,177],"other":[169,179],"feed-forward":[184],"fashion.":[185],"The":[186],"recursive":[187],"least":[188],"square":[189],"method":[190,194],"conjugate":[192],"are":[195],"calculate":[198],"weights":[201],"respectively.":[209],"Experiment":[210],"studies":[211],"four":[213],"benchmark":[214],"problems":[216],"UCI":[218],"repository":[219],"show":[220],"CG-DBLS":[222],"can":[223],"achieve":[224],"much":[225,229],"lower":[226],"error":[227],"higher":[230],"stability":[231],"than":[232],"variants.":[236]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}