{"id":"https://openalex.org/W142153452","doi":null,"title":"A bayesian decision theoretical approach to supervised learning, selective sampling, and empirical function optimization","display_name":"A bayesian decision theoretical approach to supervised learning, selective sampling, and empirical function optimization","publication_year":2010,"publication_date":"2010-01-01","ids":{"openalex":"https://openalex.org/W142153452","mag":"142153452"},"language":"en","primary_location":{"id":"mag:142153452","doi":null,"is_oa":false,"landing_page_url":"https://scholarsarchive.byu.edu/cgi/viewcontent.cgi?article=3057&context=etd","pdf_url":null,"source":null,"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":null},"volume":null,"type":"article","is_traditional":true,"indexed_in":[],"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/A5029074659","display_name":"Kevin D. Seppi","orcid":"https://orcid.org/0000-0002-4932-2700","observed_orcids":["https://orcid.org/0000-0002-4932-2700"]},"institutions":[{"id":"https://openalex.org/I100005738","display_name":"Brigham Young University","ror":"https://ror.org/047rhhm47","country_code":"US","type":"education","lineage":["https://openalex.org/I100005738"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Kevin Seppi","raw_affiliation_strings":["Brigham Young Univ"],"raw_orcid":"https://orcid.org/0000-0002-4932-2700","affiliations":[{"raw_affiliation_string":"Brigham Young Univ","institution_ids":["https://openalex.org/I100005738"]}]},{"author_position":"last","author":{"id":null,"display_name":"James L. Carroll","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I100005738","display_name":"Brigham Young University","ror":"https://ror.org/047rhhm47","country_code":"US","type":"education","lineage":["https://openalex.org/I100005738"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"James L. Carroll","raw_affiliation_strings":["Brigham Young Univ"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Brigham Young Univ","institution_ids":["https://openalex.org/I100005738"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I100005738"],"apc_list":null,"apc_paid":null,"fwci":1.2081,"has_fulltext":false,"cited_by_count":12,"citation_normalized_percentile":{"value":0.80184537,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":87,"max":97},"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12072","display_name":"Machine Learning and Algorithms","score":0.9980000257492065,"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/T12072","display_name":"Machine Learning and Algorithms","score":0.9980000257492065,"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/T11303","display_name":"Bayesian Modeling and Causal Inference","score":0.0003000000142492354,"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":0.0003000000142492354,"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/bayesian-decision-theory","display_name":"Bayesian decision theory","score":0.8840000033378601},{"id":"https://openalex.org/keywords/supervised-learning","display_name":"supervised learning","score":0.7580000162124634},{"id":"https://openalex.org/keywords/active-learning-machine-learning","display_name":"active learning (machine learning)","score":0.6660000085830688},{"id":"https://openalex.org/keywords/no-free-lunch-theorem","display_name":"no free lunch theorem","score":0.8320000171661377},{"id":"https://openalex.org/keywords/utility-functions","display_name":"utility functions","score":0.8640000224113464},{"id":"https://openalex.org/keywords/semi-supervised-learning","display_name":"semi-supervised learning","score":0.6980000138282776},{"id":"https://openalex.org/keywords/unsupervised-learning","display_name":"unsupervised learning","score":0.9919999837875366},{"id":"https://openalex.org/keywords/sample-complexity","display_name":"sample complexity","score":0.6460000276565552},{"id":"https://openalex.org/keywords/learnability","display_name":"learnability","score":0.7829999923706055},{"id":"https://openalex.org/keywords/ensembles","display_name":"ensembles","score":0.8199999928474426},{"id":"https://openalex.org/keywords/cmac","display_name":"CMAC","score":0.6859999895095825},{"id":"https://openalex.org/keywords/bayesian-neural-networks","display_name":"Bayesian neural networks","score":0.5389999747276306},{"id":"https://openalex.org/keywords/query-by-committee","display_name":"query-by-committee","score":0.6970000267028809},{"id":"https://openalex.org/keywords/expected-value-of-sample-information","display_name":"expected value of sample information","score":0.9240000247955322},{"id":"https://openalex.org/keywords/uncertainty-sampling","display_name":"uncertainty sampling","score":0.8100000023841858}],"concepts":[{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.7509125471115112},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.733020007610321},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6129817962646484},{"id":"https://openalex.org/C32254414","wikidata":"https://www.wikidata.org/wiki/Q4724364","display_name":"Algorithmic learning theory","level":3,"score":0.5770699381828308},{"id":"https://openalex.org/C58973888","wikidata":"https://www.wikidata.org/wiki/Q1041418","display_name":"Semi-supervised learning","level":2,"score":0.5746514201164246},{"id":"https://openalex.org/C24138899","wikidata":"https://www.wikidata.org/wiki/Q17141258","display_name":"Instance-based learning","level":3,"score":0.5311167240142822},{"id":"https://openalex.org/C2777723229","wikidata":"https://www.wikidata.org/wiki/Q4367921","display_name":"Learnability","level":2,"score":0.5132986903190613},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.4837200939655304},{"id":"https://openalex.org/C8038995","wikidata":"https://www.wikidata.org/wiki/Q1152135","display_name":"Unsupervised learning","level":2,"score":0.4272882044315338},{"id":"https://openalex.org/C115903097","wikidata":"https://www.wikidata.org/wiki/Q7094097","display_name":"Online machine learning","level":3,"score":0.41235360503196716},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.13280817866325378}],"mesh":[],"locations_count":1,"locations":[{"id":"mag:142153452","doi":null,"is_oa":false,"landing_page_url":"https://scholarsarchive.byu.edu/cgi/viewcontent.cgi?article=3057&context=etd","pdf_url":null,"source":null,"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":76,"referenced_works":["https://openalex.org/W14377099","https://openalex.org/W36434594","https://openalex.org/W131206777","https://openalex.org/W167016754","https://openalex.org/W173861811","https://openalex.org/W188657744","https://openalex.org/W1484084878","https://openalex.org/W1487813067","https://openalex.org/W1497256448","https://openalex.org/W1503705371","https://openalex.org/W1510052597","https://openalex.org/W1513874326","https://openalex.org/W1514940655","https://openalex.org/W1528361845","https://openalex.org/W1540706608","https://openalex.org/W1553533669","https://openalex.org/W1559615331","https://openalex.org/W1567512734","https://openalex.org/W1576808520","https://openalex.org/W1591422284","https://openalex.org/W1593345689","https://openalex.org/W1755360231","https://openalex.org/W1773803948","https://openalex.org/W1809801995","https://openalex.org/W1948236408","https://openalex.org/W1976355201","https://openalex.org/W1976526581","https://openalex.org/W1988520084","https://openalex.org/W1993740947","https://openalex.org/W2003458432","https://openalex.org/W2006258746","https://openalex.org/W2009190245","https://openalex.org/W2009207944","https://openalex.org/W2019363670","https://openalex.org/W2021367230","https://openalex.org/W2029538739","https://openalex.org/W2036242127","https://openalex.org/W2049633694","https://openalex.org/W2074064986","https://openalex.org/W2080021732","https://openalex.org/W2084544490","https://openalex.org/W2084812512","https://openalex.org/W2085989833","https://openalex.org/W2095729244","https://openalex.org/W2097264465","https://openalex.org/W2097388131","https://openalex.org/W2100483895","https://openalex.org/W2106639887","https://openalex.org/W2113816012","https://openalex.org/W2115305054","https://openalex.org/W2116199508","https://openalex.org/W2117670920","https://openalex.org/W2125358400","https://openalex.org/W2127816222","https://openalex.org/W2129444086","https://openalex.org/W2135571735","https://openalex.org/W2135892731","https://openalex.org/W2140535827","https://openalex.org/W2146096068","https://openalex.org/W2151554678","https://openalex.org/W2154952480","https://openalex.org/W2157118346","https://openalex.org/W2268893631","https://openalex.org/W2279189561","https://openalex.org/W2426031434","https://openalex.org/W2531264864","https://openalex.org/W2543580944","https://openalex.org/W2587142195","https://openalex.org/W2795495912","https://openalex.org/W2890351024","https://openalex.org/W2903158431","https://openalex.org/W2911546748","https://openalex.org/W2912934387","https://openalex.org/W2949071206","https://openalex.org/W3083113686","https://openalex.org/W3103147587"],"related_works":["https://openalex.org/W4245154941","https://openalex.org/W7095406026","https://openalex.org/W2335159923","https://openalex.org/W2147144807","https://openalex.org/W2582728387","https://openalex.org/W7095508549","https://openalex.org/W3014483210","https://openalex.org/W2168980181","https://openalex.org/W2884628527","https://openalex.org/W10673281"],"abstract_inverted_index":{"Many":[0],"have":[1],"used":[2],"the":[3,24,33,36,42,62,76,93,97,115,141,144,151,188,197,211,238,253,257,265,284],"principles":[4,286],"of":[5,23,26,35,66,96,126,171,187,192,252,283,298,367],"statistics":[6],"and":[7,57,70,86,104,164,176,204,226,245,280,295],"Bayesian":[8,44,146,262,321,328,332,345],"decision":[9],"theory":[10,163],"to":[11,20,78,99,117,213,233,240,259,264,272,291],"model":[12,34,49,79,95,131,143,157,241,258],"specific":[13],"learning":[14,27,38,59,72,121,134,194,203],"problems.":[15],"It":[16],"is":[17,32,50,64,185],"less":[18],"common":[19],"see":[21],"models":[22],"processes":[25],"in":[28,52,166,219,269],"general.":[29],"One":[30],"exception":[31],"supervised":[37,84,202,267],"process":[39],"known":[40],"as":[41,160,281],"\u201cExtended":[43],"Formalism\u201d":[45],"or":[46,150],"EBF.":[47],"This":[48],"descriptive,":[51],"that":[53,155],"it":[54],"can":[55,158,173],"describe":[56],"compare":[58],"algorithms.":[60,73],"Thus":[61],"EBF":[63,77,98,116],"capable":[65],"modeling":[67],"both":[68],"effective":[69],"ineffective":[71],"\r\nWe":[74],"extend":[75],"un-supervised":[80],"learning,":[81,83,85],"semi-supervised":[82],"empirical":[87,205,276],"function":[88,206,277],"optimization.":[89,207],"We":[90,139,208,229,247],"also":[91,230,248],"generalize":[92],"utility":[94,106,224],"deal":[100],"with":[101,105],"non-deterministic":[102],"outcomes,":[103],"functions":[107],"other":[108],"than":[109],"0-1":[110],"loss.":[111],"Finally,":[112],"we":[113,199],"modify":[114],"create":[118,292],"a":[119,161,168,179,261,274,341],"\u201cprescriptive\u201d":[120],"model,":[122],"meaning":[123],"that,":[124],"instead":[125],"describing":[127],"existing":[128],"algorithms,":[129],"our":[130],"defines":[132],"how":[133],"should":[135],"optimally":[136],"take":[137],"place.":[138],"call":[140],"resulting":[142],"Unified":[145,327,331],"Decision":[147,323,333],"Theoretical":[148,334],"Model,":[149,329,335],"UBDTM.":[152],"WE":[153],"show":[154],"this":[156],"serve":[159],"cohesive":[162],"framework":[165,184],"which":[167],"broad":[169],"range":[170],"questions":[172],"be":[174],"analyzed":[175],"studied.":[177],"Such":[178],"broadly":[180],"applicable":[181],"unified":[182],"theoretical":[183,217],"one":[186],"major":[189],"missing":[190],"ingredients":[191],"machine":[193],"theory.":[195],"\r\nUsing":[196],"UBDTM,":[198,336],"concentrate":[200],"on":[201],"then":[209],"use":[210],"UBDTM":[212,239,254],"reanalyze":[214],"many":[215],"important":[216],"issues":[218],"Machine":[220,307],"Learning,":[221,308,310,322,343,356],"including":[222],"No-Free-Lunch,":[223,339],"implications,":[225],"active":[227,304],"learning.":[228,305],"point":[231],"forward":[232],"future":[234],"directions":[235],"for":[236],"using":[237,256,303],"learnability,":[242],"sample":[243],"complexity,":[244],"ensembles.":[246],"provide":[249],"practical":[250,275],"applications":[251],"by":[255,360,363],"train":[260],"variation":[263],"CMAC":[266],"learner":[268],"closed":[270],"form,":[271],"perform":[273],"optimization":[278],"task,":[279],"part":[282],"guiding":[285],"behind":[287],"an":[288,293],"ongoing":[289],"project":[290],"electronic":[294],"print":[296],"corpus":[297],"tagged":[299],"ancient":[300],"Syriac":[301],"texts":[302],"\r\nKeywords:":[306],"Supervised":[309],"Function":[311,314,353],"Optimization,":[312,315],"Empirical":[313],"Statistics,":[316],"Bayes,":[317],"Bayes":[318],"Law,":[319],"Bayesian,":[320],"Theory,":[324,326],"Utility":[325],"UBM,":[330],"Learning":[337],"Framework,":[338],"NFL,":[340],"priori":[342],"Extended":[344],"Formalism,":[346],"EBF,":[347],"Bias,":[348,350],"Inductive":[349],"Hypothesis":[351],"Space,":[352],"Class,":[354],"Active":[355],"Uncertainty":[357],"Sampling,":[358],"Query":[359,362],"Uncertainty,":[361],"Committee,":[364],"Expected":[365],"Value":[366],"Sample":[368],"Information,":[369],"EVSI.":[370]},"counts_by_year":[{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2017,"cited_by_count":1},{"year":2015,"cited_by_count":1},{"year":2014,"cited_by_count":4},{"year":2013,"cited_by_count":2},{"year":2012,"cited_by_count":1}],"updated_date":"2026-10-16T09:59:22.235173","created_date":"2016-06-24T00:00:00"}
