{"id":"https://openalex.org/W1993078297","doi":"https://doi.org/10.1142/s0218213009000342","title":"\"DATA TEMPERATURE\" IN MINIMUM FREE ENERGIES FOR PARAMETER LEARNING OF BAYESIAN NETWORKS","display_name":"\"DATA TEMPERATURE\" IN MINIMUM FREE ENERGIES FOR PARAMETER LEARNING OF BAYESIAN NETWORKS","publication_year":2009,"publication_date":"2009-10-01","ids":{"openalex":"https://openalex.org/W1993078297","doi":"https://doi.org/10.1142/s0218213009000342","mag":"1993078297"},"language":"en","primary_location":{"id":"doi:10.1142/s0218213009000342","is_oa":false,"landing_page_url":"https://doi.org/10.1142/s0218213009000342","pdf_url":null,"source":{"id":"https://openalex.org/S178780388","display_name":"International Journal of Artificial Intelligence Tools","issn_l":"0218-2130","issn":["0218-2130","1793-6349"],"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 on Artificial Intelligence Tools","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/A5085639359","display_name":"Takashi Isozaki","orcid":"https://orcid.org/0009-0000-5697-7409"},"institutions":[{"id":"https://openalex.org/I15009632","display_name":"Fuji Xerox (Japan)","ror":"https://ror.org/02w528w58","country_code":"JP","type":"company","lineage":["https://openalex.org/I15009632"]},{"id":"https://openalex.org/I20529979","display_name":"University of Electro-Communications","ror":"https://ror.org/02x73b849","country_code":"JP","type":"education","lineage":["https://openalex.org/I20529979"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"TAKASHI ISOZAKI","raw_affiliation_strings":["Graduate School of Information Systems, The University of Electro-Communications, 1-5-1 Chofugaoka, Chofu-shi, Tokyo 182-8585, Japan","Research and Technology Group, Fuji Xerox Co. Ltd., 3-1-1 Roppongi, Minato-ku, Tokyo 106-0032, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Graduate School of Information Systems, The University of Electro-Communications, 1-5-1 Chofugaoka, Chofu-shi, Tokyo 182-8585, Japan","institution_ids":["https://openalex.org/I20529979"]},{"raw_affiliation_string":"Research and Technology Group, Fuji Xerox Co. Ltd., 3-1-1 Roppongi, Minato-ku, Tokyo 106-0032, Japan","institution_ids":["https://openalex.org/I15009632"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102128402","display_name":"Noriji Kato","orcid":null},"institutions":[{"id":"https://openalex.org/I15009632","display_name":"Fuji Xerox (Japan)","ror":"https://ror.org/02w528w58","country_code":"JP","type":"company","lineage":["https://openalex.org/I15009632"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"NORIJI KATO","raw_affiliation_strings":["Research and Technology Group, Fuji Xerox Co. Ltd., 430 Sakai, Nakai-machi, Ashigarakami-gun, Kanagawa 259-0157, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Research and Technology Group, Fuji Xerox Co. Ltd., 430 Sakai, Nakai-machi, Ashigarakami-gun, Kanagawa 259-0157, Japan","institution_ids":["https://openalex.org/I15009632"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5002549511","display_name":"Maomi Ueno","orcid":"https://orcid.org/0000-0003-3598-8867"},"institutions":[{"id":"https://openalex.org/I20529979","display_name":"University of Electro-Communications","ror":"https://ror.org/02x73b849","country_code":"JP","type":"education","lineage":["https://openalex.org/I20529979"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"MAOMI UENO","raw_affiliation_strings":["Graduate School of Information Systems, The University of Electro-Communications, 1-5-1 Chofugaoka, Chofu-shi, Tokyo 182-8585, Japan","Graduate School of Information Systems, The University of Electro-Communications, 1-5-1, Chofugaoka, Chofu-shi, Tokyo, 182-8585, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Graduate School of Information Systems, The University of Electro-Communications, 1-5-1 Chofugaoka, Chofu-shi, Tokyo 182-8585, Japan","institution_ids":["https://openalex.org/I20529979"]},{"raw_affiliation_string":"Graduate School of Information Systems, The University of Electro-Communications, 1-5-1, Chofugaoka, Chofu-shi, Tokyo, 182-8585, Japan","institution_ids":["https://openalex.org/I20529979"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.8119,"has_fulltext":false,"cited_by_count":14,"citation_normalized_percentile":{"value":0.87054959,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":"18","issue":"05","first_page":"653","last_page":"671"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11303","display_name":"Bayesian Modeling and Causal Inference","score":0.9998000264167786,"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":0.9998000264167786,"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":0.968999981880188,"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"}},{"id":"https://openalex.org/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.9506000280380249,"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/hyperparameter","display_name":"Hyperparameter","score":0.9016491174697876},{"id":"https://openalex.org/keywords/overfitting","display_name":"Overfitting","score":0.898369312286377},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.713661789894104},{"id":"https://openalex.org/keywords/principle-of-maximum-entropy","display_name":"Principle of maximum entropy","score":0.640290379524231},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.5404002070426941},{"id":"https://openalex.org/keywords/prior-probability","display_name":"Prior probability","score":0.47246623039245605},{"id":"https://openalex.org/keywords/bayesian-information-criterion","display_name":"Bayesian information criterion","score":0.46838051080703735},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.4398570656776428},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4369894564151764},{"id":"https://openalex.org/keywords/entropy","display_name":"Entropy (arrow of time)","score":0.4214104413986206},{"id":"https://openalex.org/keywords/free-energy-principle","display_name":"Free energy principle","score":0.4105130136013031},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.37981027364730835},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.3586258888244629},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.22638487815856934},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.210647314786911}],"concepts":[{"id":"https://openalex.org/C8642999","wikidata":"https://www.wikidata.org/wiki/Q4171168","display_name":"Hyperparameter","level":2,"score":0.9016491174697876},{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.898369312286377},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.713661789894104},{"id":"https://openalex.org/C9679016","wikidata":"https://www.wikidata.org/wiki/Q1417473","display_name":"Principle of maximum entropy","level":2,"score":0.640290379524231},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.5404002070426941},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.47246623039245605},{"id":"https://openalex.org/C168136583","wikidata":"https://www.wikidata.org/wiki/Q1988242","display_name":"Bayesian information criterion","level":2,"score":0.46838051080703735},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.4398570656776428},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4369894564151764},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.4214104413986206},{"id":"https://openalex.org/C33553690","wikidata":"https://www.wikidata.org/wiki/Q17014702","display_name":"Free energy principle","level":2,"score":0.4105130136013031},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.37981027364730835},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.3586258888244629},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.22638487815856934},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.210647314786911},{"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/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1142/s0218213009000342","is_oa":false,"landing_page_url":"https://doi.org/10.1142/s0218213009000342","pdf_url":null,"source":{"id":"https://openalex.org/S178780388","display_name":"International Journal of Artificial Intelligence Tools","issn_l":"0218-2130","issn":["0218-2130","1793-6349"],"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 on Artificial Intelligence Tools","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.8700000047683716,"id":"https://metadata.un.org/sdg/7","display_name":"Affordable and clean energy"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":33,"referenced_works":["https://openalex.org/W10021998","https://openalex.org/W1492518391","https://openalex.org/W1575898799","https://openalex.org/W1585743408","https://openalex.org/W1612003148","https://openalex.org/W1615454278","https://openalex.org/W1709414455","https://openalex.org/W1755360231","https://openalex.org/W1817561967","https://openalex.org/W1822300807","https://openalex.org/W1928061009","https://openalex.org/W1983690667","https://openalex.org/W1986750708","https://openalex.org/W1989926363","https://openalex.org/W2008906462","https://openalex.org/W2057393527","https://openalex.org/W2084812512","https://openalex.org/W2097725986","https://openalex.org/W2098862607","https://openalex.org/W2107042471","https://openalex.org/W2117890631","https://openalex.org/W2125631472","https://openalex.org/W2127314673","https://openalex.org/W2137587467","https://openalex.org/W2169415915","https://openalex.org/W2211621381","https://openalex.org/W2478708596","https://openalex.org/W2963331194","https://openalex.org/W4232383088","https://openalex.org/W4248700314","https://openalex.org/W4250143236","https://openalex.org/W4254165724","https://openalex.org/W4302423442"],"related_works":["https://openalex.org/W1574414179","https://openalex.org/W4362597605","https://openalex.org/W4297676672","https://openalex.org/W4281702477","https://openalex.org/W2922073769","https://openalex.org/W4378510483","https://openalex.org/W2490526372","https://openalex.org/W4298369531","https://openalex.org/W3155135229","https://openalex.org/W2767997510"],"abstract_inverted_index":{"Maximum":[0],"likelihood":[1,121],"method":[2,19,72,79,142,152,159],"for":[3,14,39,103,162],"estimating":[4],"parameters":[5,75],"of":[6,43,54,73,86,132,138,165],"Bayesian":[7,29,151],"networks":[8],"(BNs)":[9],"is":[10,27,60],"efficient":[11],"and":[12,51,96,111,123],"accurate":[13],"large":[15],"samples.":[16],"However,":[17],"the":[18,24,74,119,124,133,150,163],"suffers":[20],"from":[21],"overfitting":[22],"when":[23,56],"sample":[25],"size":[26],"small.":[28],"methods,":[30],"which":[31,98],"are":[32],"effective":[33],"to":[34],"avoid":[35],"overfitting,":[36],"present":[37],"difficulties":[38],"determining":[40],"optimal":[41],"hyperparameters":[42],"prior":[44,58],"distributions":[45],"with":[46,153],"good":[47],"balance":[48],"between":[49],"theoretical":[50],"practical":[52],"points":[53],"view":[55],"no":[57],"knowledge":[59],"available.":[61],"As":[62],"described":[63],"in":[64,84,128],"this":[65],"paper,":[66],"we":[67,105],"propose":[68,106],"an":[69],"alternative":[70],"estimation":[71],"on":[76],"BNs.":[77],"The":[78],"uses":[80],"a":[81,107,129],"principle,":[82],"rooted":[83],"thermodynamics,":[85],"minimizing":[87],"free":[88,100],"energy":[89],"(MFE).":[90],"We":[91],"define":[92],"internal":[93],"energies,":[94],"entropies,":[95],"temperature,":[97,104],"constitute":[99],"energies.":[101],"Especially":[102],"\"data":[108],"temperature\"":[109],"assumption":[110],"some":[112],"explicit":[113],"models.":[114],"This":[115],"approach":[116],"can":[117],"treat":[118],"maximum":[120,125],"principle":[122,127],"entropy":[126],"unified":[130],"manner":[131],"MFE":[134],"principle.":[135],"For":[136],"assessments":[137],"classification":[139],"accuracy,":[140],"our":[141,158],"shows":[143],"higher":[144],"accuracy":[145],"than":[146],"that":[147],"obtained":[148],"using":[149],"normally":[154],"recommended":[155],"hyperparameters.":[156,167],"Moreover,":[157],"exhibits":[160],"robustness":[161],"choice":[164],"introduced":[166]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2022,"cited_by_count":2},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":2},{"year":2017,"cited_by_count":1},{"year":2015,"cited_by_count":2},{"year":2012,"cited_by_count":2}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
