{"id":"https://openalex.org/W1947292668","doi":"https://doi.org/10.1109/ijcnn.2003.1223394","title":"Discriminative training of Bayesian Chow-Liu multinet classifiers","display_name":"Discriminative training of Bayesian Chow-Liu multinet classifiers","publication_year":2004,"publication_date":"2004-03-02","ids":{"openalex":"https://openalex.org/W1947292668","doi":"https://doi.org/10.1109/ijcnn.2003.1223394","mag":"1947292668"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn.2003.1223394","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2003.1223394","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the International Joint Conference on Neural Networks, 2003.","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/A5026022035","display_name":"Kaizhu Huang","orcid":"https://orcid.org/0000-0002-3034-9639"},"institutions":[{"id":"https://openalex.org/I177725633","display_name":"Chinese University of Hong Kong","ror":"https://ror.org/00t33hh48","country_code":"CN","type":"education","lineage":["https://openalex.org/I177725633"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Kaizhu Huang","raw_affiliation_strings":["Department of Computer Science and Engineering, Chinese University of Hong Kong, Hong Kong, China","[Department of Computer Science & Engineering, The Chinese University of Hong Kong, China]"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, Chinese University of Hong Kong, Hong Kong, China","institution_ids":["https://openalex.org/I177725633"]},{"raw_affiliation_string":"[Department of Computer Science & Engineering, The Chinese University of Hong Kong, China]","institution_ids":["https://openalex.org/I177725633"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5042251906","display_name":"Irwin King","orcid":"https://orcid.org/0000-0001-8106-6447"},"institutions":[{"id":"https://openalex.org/I177725633","display_name":"Chinese University of Hong Kong","ror":"https://ror.org/00t33hh48","country_code":"CN","type":"education","lineage":["https://openalex.org/I177725633"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"I. King","raw_affiliation_strings":["Department of Computer Science and Engineering, Chinese University of Hong Kong, Hong Kong, China","[Department of Computer Science & Engineering, The Chinese University of Hong Kong, China]"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, Chinese University of Hong Kong, Hong Kong, China","institution_ids":["https://openalex.org/I177725633"]},{"raw_affiliation_string":"[Department of Computer Science & Engineering, The Chinese University of Hong Kong, China]","institution_ids":["https://openalex.org/I177725633"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5069596903","display_name":"Michael R. Lyu","orcid":"https://orcid.org/0000-0002-3666-5798"},"institutions":[{"id":"https://openalex.org/I177725633","display_name":"Chinese University of Hong Kong","ror":"https://ror.org/00t33hh48","country_code":"CN","type":"education","lineage":["https://openalex.org/I177725633"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"M.R. Lyu","raw_affiliation_strings":["Department of Computer Science and Engineering, Chinese University of Hong Kong, Hong Kong, China","[Department of Computer Science & Engineering, The Chinese University of Hong Kong, China]"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, Chinese University of Hong Kong, Hong Kong, China","institution_ids":["https://openalex.org/I177725633"]},{"raw_affiliation_string":"[Department of Computer Science & Engineering, The Chinese University of Hong Kong, China]","institution_ids":["https://openalex.org/I177725633"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5026022035"],"corresponding_institution_ids":["https://openalex.org/I177725633"],"apc_list":null,"apc_paid":null,"fwci":3.2349,"has_fulltext":false,"cited_by_count":22,"citation_normalized_percentile":{"value":0.91926176,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":97},"biblio":{"volume":"1","issue":null,"first_page":"484","last_page":"488"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11303","display_name":"Bayesian Modeling and Causal Inference","score":0.9997000098228455,"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/T11303","display_name":"Bayesian Modeling and Causal Inference","score":0.9997000098228455,"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/T12535","display_name":"Machine Learning and Data Classification","score":0.964900016784668,"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/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.9524999856948853,"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/discriminative-model","display_name":"Discriminative model","score":0.9082254767417908},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7835612893104553},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.6944931149482727},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6545804142951965},{"id":"https://openalex.org/keywords/naive-bayes-classifier","display_name":"Naive Bayes classifier","score":0.6493024230003357},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6160776615142822},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6068283915519714},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.599909782409668},{"id":"https://openalex.org/keywords/random-subspace-method","display_name":"Random subspace method","score":0.5909680128097534},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.5810781717300415},{"id":"https://openalex.org/keywords/linear-discriminant-analysis","display_name":"Linear discriminant analysis","score":0.5498554110527039},{"id":"https://openalex.org/keywords/bayes-theorem","display_name":"Bayes' theorem","score":0.5385284423828125},{"id":"https://openalex.org/keywords/generative-model","display_name":"Generative model","score":0.5116739273071289},{"id":"https://openalex.org/keywords/posterior-probability","display_name":"Posterior probability","score":0.49535122513771057},{"id":"https://openalex.org/keywords/discriminant","display_name":"Discriminant","score":0.47213131189346313},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.38115930557250977}],"concepts":[{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.9082254767417908},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7835612893104553},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.6944931149482727},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6545804142951965},{"id":"https://openalex.org/C52001869","wikidata":"https://www.wikidata.org/wiki/Q812530","display_name":"Naive Bayes classifier","level":3,"score":0.6493024230003357},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6160776615142822},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6068283915519714},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.599909782409668},{"id":"https://openalex.org/C106135958","wikidata":"https://www.wikidata.org/wiki/Q7291993","display_name":"Random subspace method","level":3,"score":0.5909680128097534},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.5810781717300415},{"id":"https://openalex.org/C69738355","wikidata":"https://www.wikidata.org/wiki/Q1228929","display_name":"Linear discriminant analysis","level":2,"score":0.5498554110527039},{"id":"https://openalex.org/C207201462","wikidata":"https://www.wikidata.org/wiki/Q182505","display_name":"Bayes' theorem","level":3,"score":0.5385284423828125},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.5116739273071289},{"id":"https://openalex.org/C57830394","wikidata":"https://www.wikidata.org/wiki/Q278079","display_name":"Posterior probability","level":3,"score":0.49535122513771057},{"id":"https://openalex.org/C78397625","wikidata":"https://www.wikidata.org/wiki/Q192487","display_name":"Discriminant","level":2,"score":0.47213131189346313},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.38115930557250977}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/ijcnn.2003.1223394","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2003.1223394","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the International Joint Conference on Neural Networks, 2003.","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.15.5330","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.15.5330","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.cse.cuhk.edu.hk/~lyu/paper_pdf/IJCNN2003.pdf","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities","score":0.75}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":26,"referenced_works":["https://openalex.org/W1510822302","https://openalex.org/W1541396713","https://openalex.org/W1614659291","https://openalex.org/W1625504505","https://openalex.org/W1680392829","https://openalex.org/W1817561967","https://openalex.org/W1992402718","https://openalex.org/W2084134149","https://openalex.org/W2101782549","https://openalex.org/W2105594594","https://openalex.org/W2113214579","https://openalex.org/W2122412950","https://openalex.org/W2129072127","https://openalex.org/W2131702393","https://openalex.org/W2156909104","https://openalex.org/W2163166770","https://openalex.org/W2166473218","https://openalex.org/W2802533528","https://openalex.org/W3017143921","https://openalex.org/W3112020351","https://openalex.org/W4214755209","https://openalex.org/W4230674625","https://openalex.org/W6636859864","https://openalex.org/W6637386731","https://openalex.org/W6684116732","https://openalex.org/W6787685208"],"related_works":["https://openalex.org/W2320442256","https://openalex.org/W2052615004","https://openalex.org/W4241330371","https://openalex.org/W2143234973","https://openalex.org/W152772766","https://openalex.org/W4389363760","https://openalex.org/W3203470546","https://openalex.org/W1988723200","https://openalex.org/W1601560713","https://openalex.org/W2330406685"],"abstract_inverted_index":{"Discriminative":[0],"classifiers":[1,25,48,58,71],"such":[2],"as":[3,51,53,141,143],"support":[4],"vector":[5],"machines":[6],"directly":[7],"learn":[8,27],"a":[9,13,28,39,60,97,103,114,132,157],"discriminant":[10,54,70],"function":[11,131],"or":[12],"posterior":[14,40],"probability":[15,30],"model":[16,31],"to":[17,37,63,87,100,161],"perform":[18,156],"classification.":[19],"On":[20],"the":[21,65,119,129,137,147,152,163],"other":[22],"hand,":[23],"generative":[24,47,57,106],"often":[26],"joint":[29],"and":[32,154,173],"then":[33],"use":[34],"Bayes":[35],"rules":[36],"construct":[38],"classifier":[41,112],"from":[42],"this":[43,93],"model.":[44],"In":[45,92],"general,":[46],"are":[49,171],"not":[50],"accurate":[52],"classifier.":[55],"However":[56],"provide":[59],"principled":[61],"way":[62],"handle":[64],"missing":[66],"information":[67],"problems,":[68],"which":[69,135],"cannot":[72],"easily":[73],"deal":[74],"with.":[75],"To":[76],"achieve":[77],"good":[78],"performances":[79],"in":[80,113],"various":[81],"classification":[82],"tasks,":[83],"it":[84],"is":[85],"better":[86],"combine":[88],"these":[89],"two":[90],"strategies.":[91],"paper,":[94],"we":[95],"develop":[96],"novel":[98],"method":[99,126],"iteratively":[101],"train":[102],"kind":[104],"of":[105,151,159,165],"Bayesian":[107,109,121],"classifier:":[108],"Chow-Liu":[110],"multinet":[111,122],"discriminative":[115,125],"way.":[116],"Different":[117],"with":[118],"traditional":[120],"classifiers,":[123],"our":[124,166],"adds":[127],"into":[128],"optimization":[130],"penalty":[133],"item,":[134],"represents":[136],"divergence":[138],"between":[139],"classes":[140],"big":[142],"possible.":[144],"We":[145],"state":[146],"theoretical":[148],"justification,":[149],"outline":[150],"algorithm":[153],"also":[155],"series":[158],"experiments":[160,169],"demonstrate":[162],"advantages":[164],"method.":[167],"The":[168],"results":[170],"promising":[172],"encouraging.":[174]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2017,"cited_by_count":1},{"year":2016,"cited_by_count":3},{"year":2015,"cited_by_count":1},{"year":2014,"cited_by_count":3}],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2025-10-10T00:00:00"}
