{"id":"https://openalex.org/W2010271513","doi":"https://doi.org/10.1142/s0129065791000194","title":"A THREE-LAYER ADAPTIVE NETWORK FOR PATTERN DENSITY ESTIMATION AND CLASSIFICATION","display_name":"A THREE-LAYER ADAPTIVE NETWORK FOR PATTERN DENSITY ESTIMATION AND CLASSIFICATION","publication_year":1991,"publication_date":"1991-01-01","ids":{"openalex":"https://openalex.org/W2010271513","doi":"https://doi.org/10.1142/s0129065791000194","mag":"2010271513"},"language":"en","primary_location":{"id":"doi:10.1142/s0129065791000194","is_oa":false,"landing_page_url":"https://doi.org/10.1142/s0129065791000194","pdf_url":null,"source":{"id":"https://openalex.org/S197665576","display_name":"International Journal of Neural Systems","issn_l":"0129-0657","issn":["0129-0657","1793-6462"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319815","host_organization_name":"World Scientific","host_organization_lineage":["https://openalex.org/P4310319815"],"host_organization_lineage_names":["World Scientific"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Neural Systems","raw_type":"journal-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/A5079441210","display_name":"Jianxiong Wu","orcid":"https://orcid.org/0000-0001-5767-264X"},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]},{"id":"https://openalex.org/I889458895","display_name":"University of Hong Kong","ror":"https://ror.org/02zhqgq86","country_code":"HK","type":"education","lineage":["https://openalex.org/I889458895"]}],"countries":["CN","HK"],"is_corresponding":true,"raw_author_name":"Jian-xiong Wu","raw_affiliation_strings":["Department of Computer Science University of Hong Kong, Hong Kong","Institute of Image Processing and Pattern Recognition Shanghai Jiaotong University, P.R.C"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science University of Hong Kong, Hong Kong","institution_ids":["https://openalex.org/I889458895"]},{"raw_affiliation_string":"Institute of Image Processing and Pattern Recognition Shanghai Jiaotong University, P.R.C","institution_ids":["https://openalex.org/I183067930"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100894416","display_name":"Chorkin Chan","orcid":null},"institutions":[{"id":"https://openalex.org/I889458895","display_name":"University of Hong Kong","ror":"https://ror.org/02zhqgq86","country_code":"HK","type":"education","lineage":["https://openalex.org/I889458895"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Chorkin Chan","raw_affiliation_strings":["Department of Computer Science University of Hong Kong, Hong Kong","Department of Computer Science, University of Hong Kong, Hong Kong,#TAB#"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science University of Hong Kong, Hong Kong","institution_ids":["https://openalex.org/I889458895"]},{"raw_affiliation_string":"Department of Computer Science, University of Hong Kong, Hong Kong,#TAB#","institution_ids":["https://openalex.org/I889458895"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5079441210"],"corresponding_institution_ids":["https://openalex.org/I183067930","https://openalex.org/I889458895"],"apc_list":null,"apc_paid":null,"fwci":0.5177,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.71333198,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"02","issue":"03","first_page":"211","last_page":"220"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.9994999766349792,"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":0.9994999766349792,"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/T11447","display_name":"Blind Source Separation Techniques","score":0.9650999903678894,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T12676","display_name":"Machine Learning and ELM","score":0.9312000274658203,"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/estimator","display_name":"Estimator","score":0.6559905409812927},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.60903400182724},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.592979907989502},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5903265476226807},{"id":"https://openalex.org/keywords/bayes-theorem","display_name":"Bayes' theorem","score":0.54261714220047},{"id":"https://openalex.org/keywords/probability-density-function","display_name":"Probability density function","score":0.5278964638710022},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5177090167999268},{"id":"https://openalex.org/keywords/naive-bayes-classifier","display_name":"Naive Bayes classifier","score":0.5022189617156982},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.47167807817459106},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.45538946986198425},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.4472827613353729},{"id":"https://openalex.org/keywords/covariance-matrix","display_name":"Covariance matrix","score":0.4461270570755005},{"id":"https://openalex.org/keywords/covariance","display_name":"Covariance","score":0.44505253434181213},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4197903275489807},{"id":"https://openalex.org/keywords/density-estimation","display_name":"Density estimation","score":0.4190109968185425},{"id":"https://openalex.org/keywords/gaussian-function","display_name":"Gaussian function","score":0.41210705041885376},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.29645735025405884},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.15783700346946716},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.09251174330711365}],"concepts":[{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.6559905409812927},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.60903400182724},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.592979907989502},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5903265476226807},{"id":"https://openalex.org/C207201462","wikidata":"https://www.wikidata.org/wiki/Q182505","display_name":"Bayes' theorem","level":3,"score":0.54261714220047},{"id":"https://openalex.org/C197055811","wikidata":"https://www.wikidata.org/wiki/Q207522","display_name":"Probability density function","level":2,"score":0.5278964638710022},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5177090167999268},{"id":"https://openalex.org/C52001869","wikidata":"https://www.wikidata.org/wiki/Q812530","display_name":"Naive Bayes classifier","level":3,"score":0.5022189617156982},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.47167807817459106},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.45538946986198425},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4472827613353729},{"id":"https://openalex.org/C185142706","wikidata":"https://www.wikidata.org/wiki/Q1134404","display_name":"Covariance matrix","level":2,"score":0.4461270570755005},{"id":"https://openalex.org/C178650346","wikidata":"https://www.wikidata.org/wiki/Q201984","display_name":"Covariance","level":2,"score":0.44505253434181213},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4197903275489807},{"id":"https://openalex.org/C189508267","wikidata":"https://www.wikidata.org/wiki/Q17088227","display_name":"Density estimation","level":3,"score":0.4190109968185425},{"id":"https://openalex.org/C7218915","wikidata":"https://www.wikidata.org/wiki/Q1054475","display_name":"Gaussian function","level":3,"score":0.41210705041885376},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.29645735025405884},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.15783700346946716},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.09251174330711365},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1142/s0129065791000194","is_oa":false,"landing_page_url":"https://doi.org/10.1142/s0129065791000194","pdf_url":null,"source":{"id":"https://openalex.org/S197665576","display_name":"International Journal of Neural Systems","issn_l":"0129-0657","issn":["0129-0657","1793-6462"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319815","host_organization_name":"World Scientific","host_organization_lineage":["https://openalex.org/P4310319815"],"host_organization_lineage_names":["World Scientific"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Neural Systems","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2394466068","https://openalex.org/W1987683558","https://openalex.org/W2726838704","https://openalex.org/W4220802396","https://openalex.org/W2393473353","https://openalex.org/W2373790322","https://openalex.org/W2171665309","https://openalex.org/W2086889680","https://openalex.org/W2417174640","https://openalex.org/W2232109354"],"abstract_inverted_index":{"Based":[0],"on":[1,217,263],"the":[2,31,67,75,95,98,111,139,151,155,163,169,173,187,214,220,226,229,236,241,264],"assumption":[3],"that":[4,199,249],"most":[5],"probability":[6,56,157],"densities":[7,57],"in":[8,30,80,148,235],"real":[9],"life":[10],"can":[11],"be":[12,74],"approximated":[13],"by":[14,193,225],"a":[15,23,36,50,61,105,142,191,201,260],"mixture":[16],"of":[17,66,97,125,138,150,200,213,231],"Gaussian":[18,37,127],"densities,":[19],"we":[20],"propose":[21],"here":[22],"three-layer":[24],"adaptive":[25],"network":[26,99,112,115,140,170,188,215,237,242,265],"with":[27,118],"each":[28,251],"neuron":[29,89],"lower":[32],"hidden":[33,232],"layer":[34,82],"representing":[35,172],"basis":[38,68,252],"function":[39,69,159,253],"(covariance":[40],"matrix":[41],"equal":[42],"to":[43,53,90,109,180,185,254],"[Formula:":[44,70],"see":[45,71],"text]":[46,72],"where":[47],"I":[48],"is":[49],"unit":[51],"matrix)":[52],"estimate":[54],"various":[55],"and":[58,103,133,165,168,206,238],"serve":[59],"as":[60,129,131,141,190],"Bayes":[62,202],"classifier.":[63,203],"The":[64,114,136,211],"width":[65,258],"may":[73,85],"same":[76],"for":[77,100],"all":[78],"neurons":[79,233],"this":[81],"or":[83],"it":[84],"vary":[86],"from":[87,122,176],"one":[88],"another.":[91],"This":[92],"paper":[93],"investigates":[94],"effectiveness":[96],"both":[101],"cases":[102,208],"presents":[104],"localized":[106],"learning":[107],"algorithm":[108],"adjust":[110],"parameters.":[113],"was":[116,146,243,246],"trained":[117],"artificial":[119],"data":[120,167],"derived":[121],"known":[123],"mixtures":[124,178],"memoryless":[126],"sources":[128],"well":[130,219],"exponential":[132],"Gamma":[134],"densities.":[135],"performance":[137],"pattern":[143],"density":[144,158],"estimator":[145],"measured":[147],"terms":[149],"relative":[152],"difference":[153],"between":[154],"target":[156,221],"(p.d.f.)":[160],"which":[161],"generates":[162],"training":[164,227],"testing":[166],"output":[171],"estimation.":[174],"Samples":[175],"two":[177,181],"corresponding":[179],"classes":[182],"were":[183,209,223],"used":[184],"test":[186],"capability":[189],"classifier":[192],"comparing":[194],"its":[195],"error":[196],"rate":[197],"against":[198],"Both":[204],"one-":[205],"two-dimensional":[207],"explored.":[210],"successfulness":[212],"depended":[216],"how":[218,239],"p.d.f.\u2019s":[222],"represented":[224],"samples,":[228],"number":[230],"employed":[234],"thoroughly":[240],"trained.":[244],"It":[245],"also":[247],"found":[248],"allowing":[250],"have":[255],"an":[256],"independent":[257],"had":[259],"predominant":[261],"effect":[262],"performance.":[266]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}