{"id":"https://openalex.org/W1974755789","doi":"https://doi.org/10.1162/089976602760128090","title":"On the Problem in Model Selection of Neural Network Regression in Overrealizable Scenario","display_name":"On the Problem in Model Selection of Neural Network Regression in Overrealizable Scenario","publication_year":2002,"publication_date":"2002-08-01","ids":{"openalex":"https://openalex.org/W1974755789","doi":"https://doi.org/10.1162/089976602760128090","mag":"1974755789","pmid":"https://pubmed.ncbi.nlm.nih.gov/12180410"},"language":"en","primary_location":{"is_oa":false,"landing_page_url":"https://doi.org/10.1162/089976602760128090","pdf_url":null,"source":{"id":"https://openalex.org/S207023548","display_name":"Neural Computation","issn_l":"0899-7667","issn":["0899-7667","1530-888X"],"is_oa":false,"is_in_doaj":false,"is_indexed_in_scopus":true,"is_core":true,"host_organization":"https://openalex.org/P4310315718","host_organization_name":"The MIT Press","host_organization_lineage":["https://openalex.org/P4310315718"],"host_organization_lineage_names":["The MIT Press"],"type":"journal"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false},"type":"article","type_crossref":"journal-article","indexed_in":["crossref","pubmed"],"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/A5108357188","display_name":"Katsuyuki Hagiwara","orcid":null},"institutions":[{"id":"https://openalex.org/I178574317","display_name":"Mie University","ror":"https://ror.org/01529vy56","country_code":"JP","type":"funder","lineage":["https://openalex.org/I178574317"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Katsuyuki Hagiwara","raw_affiliation_strings":["Faculty of Physics Engineering, Mie University, Tsu, 514-8507, Japan,"],"affiliations":[{"raw_affiliation_string":"Faculty of Physics Engineering, Mie University, Tsu, 514-8507, Japan,","institution_ids":["https://openalex.org/I178574317"]}]}],"institution_assertions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5108357188"],"corresponding_institution_ids":["https://openalex.org/I178574317"],"apc_list":null,"apc_paid":null,"fwci":4.968,"has_fulltext":true,"fulltext_origin":"ngrams","cited_by_count":67,"citation_normalized_percentile":{"value":0.90245,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":95},"biblio":{"volume":"14","issue":"8","first_page":"1979","last_page":"2002"},"is_retracted":false,"is_paratext":false,"primary_topic":{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.9985,"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":0.9985,"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":0.9958,"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/T10876","display_name":"Fault Detection and Control Systems","score":0.9904,"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":[],"concepts":[{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.66295034},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.6461476},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.58900136},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5246846},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.519035},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.45299506},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.4494002},{"id":"https://openalex.org/C93959086","wikidata":"https://www.wikidata.org/wiki/Q6888345","display_name":"Model selection","level":2,"score":0.43531466},{"id":"https://openalex.org/C3832189","wikidata":"https://www.wikidata.org/wiki/Q8588916","display_name":"Models of neural computation","level":3,"score":0.41455024},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.28618464},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.24113429}],"mesh":[],"locations_count":2,"locations":[{"is_oa":false,"landing_page_url":"https://doi.org/10.1162/089976602760128090","pdf_url":null,"source":{"id":"https://openalex.org/S207023548","display_name":"Neural Computation","issn_l":"0899-7667","issn":["0899-7667","1530-888X"],"is_oa":false,"is_in_doaj":false,"is_indexed_in_scopus":true,"is_core":true,"host_organization":"https://openalex.org/P4310315718","host_organization_name":"The MIT Press","host_organization_lineage":["https://openalex.org/P4310315718"],"host_organization_lineage_names":["The MIT Press"],"type":"journal"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false},{"is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/12180410","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_indexed_in_scopus":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":["National Institutes of Health"],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false}],"best_oa_location":null,"sustainable_development_goals":[],"grants":[],"datasets":[],"versions":[],"referenced_works_count":30,"referenced_works":["https://openalex.org/W1506069954","https://openalex.org/W1554663460","https://openalex.org/W1562777741","https://openalex.org/W1582314917","https://openalex.org/W165370603","https://openalex.org/W171686316","https://openalex.org/W1968908999","https://openalex.org/W1970789124","https://openalex.org/W1980620433","https://openalex.org/W1988485873","https://openalex.org/W1995842804","https://openalex.org/W2008430743","https://openalex.org/W2027016402","https://openalex.org/W2054833248","https://openalex.org/W2058815839","https://openalex.org/W2070681743","https://openalex.org/W2079356438","https://openalex.org/W2086236873","https://openalex.org/W2098545770","https://openalex.org/W2109246257","https://openalex.org/W2120217353","https://openalex.org/W2125074013","https://openalex.org/W2147147647","https://openalex.org/W2147299889","https://openalex.org/W2168175751","https://openalex.org/W2169945273","https://openalex.org/W2897129259","https://openalex.org/W3016197797","https://openalex.org/W4242688646","https://openalex.org/W4285719527"],"related_works":["https://openalex.org/W4381571012","https://openalex.org/W4289356671","https://openalex.org/W3186837933","https://openalex.org/W31220157","https://openalex.org/W2389155397","https://openalex.org/W2368989808","https://openalex.org/W2355687852","https://openalex.org/W2312753042","https://openalex.org/W2165884543","https://openalex.org/W2034959125"],"abstract_inverted_index":{"In":[0,90],"considering":[1],"a":[2,74,83,142,156,251,267,272,277],"statistical":[3],"model":[4,24,278],"selection":[5,25,279],"of":[6,19,31,52,77,106,110,118,121,124,136,145,158,178,193,211,241,262],"neural":[7,53,214,254],"networks":[8,54,112,215,255],"and":[9,47,55,62,141,170,189,216,256],"radial":[10,56,217],"basis":[11,57,218],"functions":[12,58,219],"under":[13,94],"an":[14,28,78,133,282],"overrealizable":[15,60,79,283],"case,":[16],"the":[17,23,32,37,43,48,64,91,100,104,107,116,119,122,137,146,151,167,171,179,190,194,209,232,239,260,263],"problem":[18],"unidentifiability":[20],"emerges.":[21],"Because":[22],"criterion":[26,280],"is":[27,113,153,183,198,220,235,271],"unbiased":[29],"estimator":[30],"generalization":[33,50,148,152,172,196,233],"error":[34,46,51,140,169,182,197],"based":[35],"on":[36,166],"training":[38,45,88,139,159,168,181],"error,":[39,149],"this":[40,95,129,289,295],"article":[41,245],"analyzes":[42],"expected":[44,49,108,138,147,180,195],"in":[59,102,186,201,213,223,238,259,266,275,281,288,294],"cases":[61],"clarifies":[63],"difference":[65,252],"from":[66],"regular":[67,187,202,224,242,257],"models,":[68,188],"for":[69],"which":[70,103],"identifiability":[71],"holds.":[72],"As":[73],"special":[75],"case":[76,240],"scenario,":[80],"we":[81,97,131,162],"assumed":[82],"gaussian":[84],"noise":[85],"sequence":[86],"as":[87],"data.":[89],"least-squares":[92,264],"estimation":[93,265],"assumption,":[96],"first":[98,273],"formulated":[99],"problem,":[101],"calculation":[105,117],"errors":[109],"unidentifiable":[111],"reduced":[114],"to":[115,249],"expectation":[120],"supremum":[123],"thex":[125],"2":[126],"process.":[127],"Under":[128],"formulation,":[130],"gave":[132,163],"upper":[134,176],"bound":[135,144,177,192],"lower":[143,191],"where":[150],"measured":[154],"at":[155],"set":[157],"inputs.":[160],"Furthermore,":[161],"stochastic":[164],"bounds":[165],"error.":[173],"The":[174,204,244],"obtained":[175],"smaller":[184],"than":[185,200,222,237],"larger":[199],"models.":[203,225,243],"result":[205],"tells":[206,229],"us":[207,230],"that":[208,231],"degree":[210],"overfitting":[212],"higher":[221],"Correspondingly,":[226],"it":[227],"also":[228,292],"capability":[234],"worse":[236],"may":[246],"be":[247],"enough":[248],"show":[250],"between":[253],"models":[258],"context":[261],"simple":[268],"situation.":[269],"This":[270],"step":[274],"constructing":[276],"case.":[284],"Further":[285],"important":[286],"problems":[287],"direction":[290],"are":[291],"included":[293],"article.":[296]},"abstract_inverted_index_v3":null,"cited_by_api_url":"https://api.openalex.org/works?filter=cites:W1974755789","counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2019,"cited_by_count":26},{"year":2017,"cited_by_count":1},{"year":2016,"cited_by_count":1},{"year":2015,"cited_by_count":1},{"year":2012,"cited_by_count":2}],"updated_date":"2025-04-19T05:39:09.275135","created_date":"2016-06-24"}