{"id":"https://openalex.org/W2101751459","doi":"https://doi.org/10.1109/ijcnn.1999.831174","title":"Neural networks to estimate ML multi-class constrained conditional probability density functions","display_name":"Neural networks to estimate ML multi-class constrained conditional probability density functions","publication_year":2003,"publication_date":"2003-01-22","ids":{"openalex":"https://openalex.org/W2101751459","doi":"https://doi.org/10.1109/ijcnn.1999.831174","mag":"2101751459"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn.1999.831174","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.1999.831174","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IJCNN'99. International Joint Conference on Neural Networks. Proceedings (Cat. No.99CH36339)","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/A5061212034","display_name":"Juan Ignacio Arribas","orcid":"https://orcid.org/0000-0002-7486-6152"},"institutions":[{"id":"https://openalex.org/I108103353","display_name":"Universidad de Valladolid","ror":"https://ror.org/01fvbaw18","country_code":"ES","type":"education","lineage":["https://openalex.org/I108103353"]}],"countries":["ES"],"is_corresponding":false,"raw_author_name":"J. Ignacio Arribas","raw_affiliation_strings":["Department of Teor\u00eda de la Se\u00f1ial, Comunicaciones e Ing. Telem\u00e1tica, Universidad de Valladolid, Valladolid, Spain","Dept. of Teorica de la Senal, Comunicaciones e Ing. Telematica, Valladolid Univ., Spain"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Teor\u00eda de la Se\u00f1ial, Comunicaciones e Ing. Telem\u00e1tica, Universidad de Valladolid, Valladolid, Spain","institution_ids":["https://openalex.org/I108103353"]},{"raw_affiliation_string":"Dept. of Teorica de la Senal, Comunicaciones e Ing. Telematica, Valladolid Univ., Spain","institution_ids":["https://openalex.org/I108103353"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5030068883","display_name":"Jes\u00fas Cid\u2010Sueiro","orcid":"https://orcid.org/0000-0002-5243-5992"},"institutions":[{"id":"https://openalex.org/I108103353","display_name":"Universidad de Valladolid","ror":"https://ror.org/01fvbaw18","country_code":"ES","type":"education","lineage":["https://openalex.org/I108103353"]}],"countries":["ES"],"is_corresponding":false,"raw_author_name":"J. Cid-Sueiro","raw_affiliation_strings":["Department of Teor\u00eda de la Se\u00f1ial, Comunicaciones e Ing. Telem\u00e1tica, Universidad de Valladolid, Valladolid, Spain","University of Valladolid"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Teor\u00eda de la Se\u00f1ial, Comunicaciones e Ing. Telem\u00e1tica, Universidad de Valladolid, Valladolid, Spain","institution_ids":["https://openalex.org/I108103353"]},{"raw_affiliation_string":"University of Valladolid","institution_ids":["https://openalex.org/I108103353"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5060798483","display_name":"T\u00fclay Adal\u0131","orcid":"https://orcid.org/0000-0003-0594-2796"},"institutions":[{"id":"https://openalex.org/I79272384","display_name":"University of Maryland, Baltimore County","ror":"https://ror.org/02qskvh78","country_code":"US","type":"education","lineage":["https://openalex.org/I79272384"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"T. Adali","raw_affiliation_strings":["Department of Computer Science and Electrical Engineering, University of Maryland Baltimore County, Baltimore, MD, USA","\u00a0 University of Maryland, Baltimore County"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Electrical Engineering, University of Maryland Baltimore County, Baltimore, MD, USA","institution_ids":["https://openalex.org/I79272384"]},{"raw_affiliation_string":"\u00a0 University of Maryland, Baltimore County","institution_ids":["https://openalex.org/I79272384"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5046532092","display_name":"An\u0131\u0301bal R. Figueiras-Vidal","orcid":"https://orcid.org/0000-0001-7068-9884"},"institutions":[{"id":"https://openalex.org/I50357001","display_name":"Universidad Carlos III de Madrid","ror":"https://ror.org/03ths8210","country_code":"ES","type":"education","lineage":["https://openalex.org/I50357001"]}],"countries":["ES"],"is_corresponding":false,"raw_author_name":"A.R. Figueiras-Vidal","raw_affiliation_strings":["Department of Tecnolog\u00edas de las Comunicaciones, Universidad Carlos III de Madrid, Madrid, Spain","Charles III University of Madrid"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Tecnolog\u00edas de las Comunicaciones, Universidad Carlos III de Madrid, Madrid, Spain","institution_ids":["https://openalex.org/I50357001"]},{"raw_affiliation_string":"Charles III University of Madrid","institution_ids":["https://openalex.org/I50357001"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.2554,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.51144318,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":"2","issue":null,"first_page":"1429","last_page":"1432"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.9990000128746033,"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/T10320","display_name":"Neural Networks and Applications","score":0.9990000128746033,"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/T10711","display_name":"Target Tracking and Data Fusion in Sensor Networks","score":0.9846000075340271,"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/T10876","display_name":"Fault Detection and Control Systems","score":0.9779999852180481,"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/softmax-function","display_name":"Softmax function","score":0.8019629120826721},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6369068622589111},{"id":"https://openalex.org/keywords/perceptron","display_name":"Perceptron","score":0.623768150806427},{"id":"https://openalex.org/keywords/maximum-a-posteriori-estimation","display_name":"Maximum a posteriori estimation","score":0.6205012202262878},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.6084566116333008},{"id":"https://openalex.org/keywords/density-estimation","display_name":"Density estimation","score":0.5896865725517273},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.529385507106781},{"id":"https://openalex.org/keywords/bayes-theorem","display_name":"Bayes' theorem","score":0.5096426010131836},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.46851846575737},{"id":"https://openalex.org/keywords/conditional-probability-distribution","display_name":"Conditional probability distribution","score":0.46716368198394775},{"id":"https://openalex.org/keywords/a-priori-and-a-posteriori","display_name":"A priori and a posteriori","score":0.4606073200702667},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.4429197907447815},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4366511404514313},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4285556674003601},{"id":"https://openalex.org/keywords/conditional-probability","display_name":"Conditional probability","score":0.4142104983329773},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.40074020624160767},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2859737277030945},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.23115003108978271},{"id":"https://openalex.org/keywords/maximum-likelihood","display_name":"Maximum likelihood","score":0.1324552595615387},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.1015048623085022}],"concepts":[{"id":"https://openalex.org/C188441871","wikidata":"https://www.wikidata.org/wiki/Q7554146","display_name":"Softmax function","level":3,"score":0.8019629120826721},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6369068622589111},{"id":"https://openalex.org/C60908668","wikidata":"https://www.wikidata.org/wiki/Q690207","display_name":"Perceptron","level":3,"score":0.623768150806427},{"id":"https://openalex.org/C9810830","wikidata":"https://www.wikidata.org/wiki/Q635384","display_name":"Maximum a posteriori estimation","level":3,"score":0.6205012202262878},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6084566116333008},{"id":"https://openalex.org/C189508267","wikidata":"https://www.wikidata.org/wiki/Q17088227","display_name":"Density estimation","level":3,"score":0.5896865725517273},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.529385507106781},{"id":"https://openalex.org/C207201462","wikidata":"https://www.wikidata.org/wiki/Q182505","display_name":"Bayes' theorem","level":3,"score":0.5096426010131836},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.46851846575737},{"id":"https://openalex.org/C43555835","wikidata":"https://www.wikidata.org/wiki/Q2300258","display_name":"Conditional probability distribution","level":2,"score":0.46716368198394775},{"id":"https://openalex.org/C75553542","wikidata":"https://www.wikidata.org/wiki/Q178161","display_name":"A priori and a posteriori","level":2,"score":0.4606073200702667},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.4429197907447815},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4366511404514313},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4285556674003601},{"id":"https://openalex.org/C44492722","wikidata":"https://www.wikidata.org/wiki/Q327069","display_name":"Conditional probability","level":2,"score":0.4142104983329773},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.40074020624160767},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2859737277030945},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.23115003108978271},{"id":"https://openalex.org/C49781872","wikidata":"https://www.wikidata.org/wiki/Q1045555","display_name":"Maximum likelihood","level":2,"score":0.1324552595615387},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.1015048623085022},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.0},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","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}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/ijcnn.1999.831174","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.1999.831174","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IJCNN'99. International Joint Conference on Neural Networks. Proceedings (Cat. No.99CH36339)","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.310.1230","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.310.1230","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.csee.umbc.edu/~adali/pubs/IEEEpubs/ijcnn99arribas_multiclass.pdf","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":6,"referenced_works":["https://openalex.org/W2118813157","https://openalex.org/W2127570188","https://openalex.org/W2137054688","https://openalex.org/W2141278204","https://openalex.org/W2153627206","https://openalex.org/W6679974894"],"related_works":["https://openalex.org/W4233361406","https://openalex.org/W1523756637","https://openalex.org/W2094114678","https://openalex.org/W2136165559","https://openalex.org/W4297630244","https://openalex.org/W2348834827","https://openalex.org/W2366186655","https://openalex.org/W3201445190","https://openalex.org/W1569764406","https://openalex.org/W1964363270"],"abstract_inverted_index":{"A":[0],"new":[1],"algorithm,":[2],"the":[3,15,20,33,49,53,59,64,67,93,102,110,114,117,133],"joint":[4],"network":[5,111,121],"and":[6,52,113,128,152],"data":[7,118,129],"density":[8,37],"estimation":[9,34],"(JNDDE),":[10],"is":[11,30,77],"proposed":[12],"to":[13,57,79,149],"estimate":[14],"'a":[16],"posteriori'":[17],"probabilities":[18,62],"of":[19,35,66,84,104,116],"targets":[21],"with":[22,42],"neural":[23],"networks":[24],"in":[25,155],"multiple":[26],"classes":[27],"problems.":[28],"It":[29],"based":[31,138],"on":[32,125,132],"conditional":[36],"functions":[38],"for":[39,92,142],"each":[40],"class":[41],"some":[43],"restrictions":[44],"or":[45,123],"constraints":[46],"imposed":[47],"by":[48,82],"classifier":[50],"structure":[51],"use":[54],"Bayes":[55],"rule":[56],"force":[58],"a":[60,72,89,106,156],"posteriori":[61],"at":[63],"output":[65],"network,":[68],"known":[69],"here":[70],"as":[71,88],"implicit":[73],"set.":[74],"The":[75,99],"method":[76,100],"applied":[78,148],"train":[80],"perceptrons":[81],"means":[83],"Gaussian":[85],"mixture":[86],"inputs,":[87],"particular":[90],"example":[91],"generalized":[94],"Softmax":[95],"perceptron":[96],"(GSP)":[97],"network.":[98],"has":[101],"advantage":[103],"providing":[105],"clear":[107],"distinction":[108],"between":[109],"architecture":[112],"model":[115],"constraints,":[119],"giving":[120],"parameters":[122,131],"weights":[124],"one":[126],"side":[127],"over":[130],"other.":[134],"MLE":[135],"stochastic":[136],"gradient":[137],"rules":[139],"are":[140],"obtained":[141],"JNDDE.":[143],"This":[144],"algorithm":[145],"can":[146],"be":[147],"hybrid":[150],"labeled":[151],"unlabeled":[153],"learning":[154],"natural":[157],"fashion.":[158]},"counts_by_year":[{"year":2019,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}