{"id":"https://openalex.org/W2146403040","doi":"https://doi.org/10.1109/tit.1972.1054863","title":"Considerations of sample and feature size","display_name":"Considerations of sample and feature size","publication_year":1972,"publication_date":"1972-09-01","ids":{"openalex":"https://openalex.org/W2146403040","doi":"https://doi.org/10.1109/tit.1972.1054863","mag":"2146403040"},"language":"en","primary_location":{"id":"doi:10.1109/tit.1972.1054863","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tit.1972.1054863","pdf_url":null,"source":{"id":"https://openalex.org/S4502562","display_name":"IEEE Transactions on Information Theory","issn_l":"0018-9448","issn":["0018-9448","1557-9654"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Information Theory","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/A5035085542","display_name":"Donald H. Foley","orcid":null},"institutions":[{"id":"https://openalex.org/I70983195","display_name":"Syracuse University","ror":"https://ror.org/025r5qe02","country_code":"US","type":"education","lineage":["https://openalex.org/I70983195"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"D. Foley","raw_affiliation_strings":["Department of Systemsand Information Sciences, Syracuse University, Syracuse, NY, USA","[Department of Systemsand Information Sciences, Syracuse University, Syracuse, NY, USA]"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Systemsand Information Sciences, Syracuse University, Syracuse, NY, USA","institution_ids":["https://openalex.org/I70983195"]},{"raw_affiliation_string":"[Department of Systemsand Information Sciences, Syracuse University, Syracuse, NY, USA]","institution_ids":["https://openalex.org/I70983195"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5035085542"],"corresponding_institution_ids":["https://openalex.org/I70983195"],"apc_list":null,"apc_paid":null,"fwci":8.5826,"has_fulltext":false,"cited_by_count":293,"citation_normalized_percentile":{"value":0.97738177,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":"18","issue":"5","first_page":"618","last_page":"626"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.9954000115394592,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T10057","display_name":"Face and Expression Recognition","score":0.9954000115394592,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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":0.9873999953269958,"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/T10320","display_name":"Neural Networks and Applications","score":0.9865000247955322,"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/word-error-rate","display_name":"Word error rate","score":0.6532895565032959},{"id":"https://openalex.org/keywords/bayes-error-rate","display_name":"Bayes error rate","score":0.6246535778045654},{"id":"https://openalex.org/keywords/bayes-theorem","display_name":"Bayes' theorem","score":0.5201785564422607},{"id":"https://openalex.org/keywords/sample-size-determination","display_name":"Sample size determination","score":0.5056719779968262},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.46650001406669617},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4590193033218384},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.44981247186660767},{"id":"https://openalex.org/keywords/test-set","display_name":"Test set","score":0.445374995470047},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.38781529664993286},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.3776357173919678},{"id":"https://openalex.org/keywords/bayes-classifier","display_name":"Bayes classifier","score":0.3613537549972534},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.15109696984291077}],"concepts":[{"id":"https://openalex.org/C40969351","wikidata":"https://www.wikidata.org/wiki/Q3516228","display_name":"Word error rate","level":2,"score":0.6532895565032959},{"id":"https://openalex.org/C143809311","wikidata":"https://www.wikidata.org/wiki/Q4874458","display_name":"Bayes error rate","level":5,"score":0.6246535778045654},{"id":"https://openalex.org/C207201462","wikidata":"https://www.wikidata.org/wiki/Q182505","display_name":"Bayes' theorem","level":3,"score":0.5201785564422607},{"id":"https://openalex.org/C129848803","wikidata":"https://www.wikidata.org/wiki/Q2564360","display_name":"Sample size determination","level":2,"score":0.5056719779968262},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.46650001406669617},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4590193033218384},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.44981247186660767},{"id":"https://openalex.org/C169903167","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Test set","level":2,"score":0.445374995470047},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.38781529664993286},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.3776357173919678},{"id":"https://openalex.org/C185207860","wikidata":"https://www.wikidata.org/wiki/Q17004744","display_name":"Bayes classifier","level":4,"score":0.3613537549972534},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.15109696984291077}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tit.1972.1054863","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tit.1972.1054863","pdf_url":null,"source":{"id":"https://openalex.org/S4502562","display_name":"IEEE Transactions on Information Theory","issn_l":"0018-9448","issn":["0018-9448","1557-9654"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Information Theory","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":14,"referenced_works":["https://openalex.org/W1979249878","https://openalex.org/W1983893626","https://openalex.org/W1999584558","https://openalex.org/W2050173378","https://openalex.org/W2065283057","https://openalex.org/W2073127487","https://openalex.org/W2079100340","https://openalex.org/W2090562109","https://openalex.org/W2098057602","https://openalex.org/W2161278885","https://openalex.org/W2166943296","https://openalex.org/W2168396487","https://openalex.org/W2904853484","https://openalex.org/W4238049555"],"related_works":["https://openalex.org/W2057359786","https://openalex.org/W1986699031","https://openalex.org/W1973600295","https://openalex.org/W1969985447","https://openalex.org/W1599512561","https://openalex.org/W2034570513","https://openalex.org/W2041448365","https://openalex.org/W2006328505","https://openalex.org/W891638917","https://openalex.org/W2537862391"],"abstract_inverted_index":{"In":[0,75],"many":[1],"practical":[2],"pattern-classification":[3],"problems":[4],"the":[5,14,21,36,40,48,53,55,78,96,110,116,124,135,142,154,157],"underlying":[6],"probability":[7],"distributions":[8,89],"are":[9],"not":[10],"completely":[11],"known.":[12],"Consequently,":[13],"classification":[15],"logic":[16],"must":[17],"be":[18],"determined":[19],"on":[20,39],"basis":[22],"of":[23,47,52,57,62,95,133,144,156],"vector":[24],"samples":[25,145],"gathered":[26],"for":[27,82],"each":[28],"class.":[29],"Although":[30],"it":[31],"is":[32,43,90,106,121,128,161,167],"common":[33],"knowledge":[34],"that":[35,123,166],"error":[37,50,80,104,113,118,126,139,159],"rate":[38,51,81,105,114,127,140,160],"design":[41],"set":[42],"a":[44,60,83,93,164],"biased":[45,131],"estimate":[46,132],"true":[49],"classifier,":[54],"amount":[56],"bias":[58],"as":[59,92],"function":[61,94,165],"sample":[63,97],"size":[64,69,98],"per":[65,99,146],"class":[66,147],"and":[67,115],"feature":[68],"has":[70],"been":[71],"an":[72,129],"open":[73],"question.":[74],"this":[76],"paper,":[77],"design-set":[79,103,125,158],"two-class":[84],"problem":[85],"with":[86],"multivariate":[87],"normal":[88],"derived":[91],"class(N)and":[100],"dimensionality(L).":[101],"The":[102],"compared":[107],"to":[108,148],"both":[109],"corresponding":[111],"Bayes":[112,136],"test-set":[117,138],"rate.":[119],"It":[120],"demonstrated":[122],"extremely":[130],"either":[134],"or":[137],"if":[141],"ratio":[143],"dimensions(N/L)is":[149],"less":[150],"than":[151],"three.":[152],"Also":[153],"variance":[155],"approximated":[162],"by":[163],"bounded":[168],"by1/8N.":[169]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":6},{"year":2021,"cited_by_count":4},{"year":2020,"cited_by_count":5},{"year":2019,"cited_by_count":3},{"year":2018,"cited_by_count":4},{"year":2017,"cited_by_count":2},{"year":2016,"cited_by_count":2},{"year":2015,"cited_by_count":4},{"year":2014,"cited_by_count":2},{"year":2013,"cited_by_count":12},{"year":2012,"cited_by_count":9}],"updated_date":"2026-07-29T09:40:50.615796","created_date":"2025-10-10T00:00:00"}
