{"id":"https://openalex.org/W2160920198","doi":null,"title":"Assessing and Improving Neural Network Predictions by the Bootstrap Algorithm","display_name":"Assessing and Improving Neural Network Predictions by the Bootstrap Algorithm","publication_year":1992,"publication_date":"1992-11-30","ids":{"openalex":"https://openalex.org/W2160920198","mag":"2160920198"},"language":"en","primary_location":{"id":"pmh:oai:CiteSeerX.psu:10.1.1.840.6109","doi":null,"is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.840.6109","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://papers.nips.cc/paper/659-assessing-and-improving-neural-network-predictions-by-the-bootstrap-algorithm.pdf","raw_type":"text"},"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/A5025445408","display_name":"Gerhard Paa\u00df","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Gerhard Paa\u00df","raw_affiliation_strings":["German National Research Center for Computer Science, Sankt Augustin, Germany#TAB#"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"German National Research Center for Computer Science, Sankt Augustin, Germany#TAB#","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":["https://openalex.org/A5025445408"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.778,"has_fulltext":false,"cited_by_count":33,"citation_normalized_percentile":{"value":0.77540235,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":88,"max":97},"biblio":{"volume":"5","issue":null,"first_page":"196","last_page":"203"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.850600004196167,"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":0.850600004196167,"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/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.05040000006556511,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.012900000438094139,"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/bootstrap-algorithm","display_name":"bootstrap algorithm","score":0.8759999871253967},{"id":"https://openalex.org/keywords/neural-network","display_name":"artificial neural networks","score":0.8640000224113464},{"id":"https://openalex.org/keywords/predictive-distribution","display_name":"predictive distribution","score":0.777999997138977},{"id":"https://openalex.org/keywords/nonparametric-confidence-intervals","display_name":"nonparametric confidence intervals","score":0.9480000138282776},{"id":"https://openalex.org/keywords/consistency","display_name":"consistency","score":0.5120000243186951},{"id":"https://openalex.org/keywords/convergence-rate","display_name":"convergence rate","score":0.9129999876022339},{"id":"https://openalex.org/keywords/forecast-uncertainty","display_name":"forecast uncertainty","score":0.3269999921321869}],"concepts":[{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.7576130032539368},{"id":"https://openalex.org/C102366305","wikidata":"https://www.wikidata.org/wiki/Q1097688","display_name":"Nonparametric statistics","level":2,"score":0.7071106433868408},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6906320452690125},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.642950177192688},{"id":"https://openalex.org/C2777303404","wikidata":"https://www.wikidata.org/wiki/Q759757","display_name":"Convergence (economics)","level":2,"score":0.6232641935348511},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.6193825006484985},{"id":"https://openalex.org/C162040801","wikidata":"https://www.wikidata.org/wiki/Q799897","display_name":"Bootstrap aggregating","level":2,"score":0.514351487159729},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5021843910217285},{"id":"https://openalex.org/C44249647","wikidata":"https://www.wikidata.org/wiki/Q208498","display_name":"Confidence interval","level":2,"score":0.46487119793891907},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.4152318239212036},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3733803629875183},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.36870622634887695},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.26381051540374756},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.24121245741844177},{"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/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C50522688","wikidata":"https://www.wikidata.org/wiki/Q189833","display_name":"Economic growth","level":1,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"pmh:oai:CiteSeerX.psu:10.1.1.840.6109","doi":null,"is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.840.6109","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://papers.nips.cc/paper/659-assessing-and-improving-neural-network-predictions-by-the-bootstrap-algorithm.pdf","raw_type":"text"},{"id":"pmh:oai:fraunhofer.de:N-33458","doi":null,"is_oa":false,"landing_page_url":"http://publica.fraunhofer.de/documents/N-33458.html","pdf_url":null,"source":{"id":"https://openalex.org/S4306400801","display_name":"Publikationsdatenbank der Fraunhofer-Gesellschaft (Fraunhofer-Gesellschaft)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I4923324","host_organization_name":"Fraunhofer-Gesellschaft","host_organization_lineage":["https://openalex.org/I4923324"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Fraunhofer AIS","raw_type":"Conference Paper"},{"id":"pmh:oai:publica.fraunhofer.de:publica/320511","doi":null,"is_oa":false,"landing_page_url":"https://publica.fraunhofer.de/handle/publica/320511","pdf_url":null,"source":{"id":"https://openalex.org/S4306400318","display_name":"Fraunhofer-Publica (Fraunhofer-Gesellschaft)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I4923324","host_organization_name":"Fraunhofer-Gesellschaft","host_organization_lineage":["https://openalex.org/I4923324"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"conference paper"},{"id":"mag:2160920198","doi":null,"is_oa":false,"landing_page_url":"https://papers.nips.cc/paper/659-assessing-and-improving-neural-network-predictions-by-the-bootstrap-algorithm.pdf","pdf_url":null,"source":{"id":"https://openalex.org/S4363606243","display_name":"neural information processing systems","issn_l":"1049-5258","issn":["1049-5258"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core","norway-2"],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":"Neural Information Processing Systems","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":20,"referenced_works":["https://openalex.org/W1973554447","https://openalex.org/W1976927254","https://openalex.org/W2009067475","https://openalex.org/W2011626015","https://openalex.org/W2043415670","https://openalex.org/W2051726817","https://openalex.org/W2067707835","https://openalex.org/W2069888879","https://openalex.org/W2076167730","https://openalex.org/W2101424588","https://openalex.org/W2104499761","https://openalex.org/W2112688502","https://openalex.org/W2117897510","https://openalex.org/W2124181495","https://openalex.org/W2139763837","https://openalex.org/W2146767532","https://openalex.org/W2514569044","https://openalex.org/W3026721701","https://openalex.org/W3146087985","https://openalex.org/W4241830588"],"related_works":["https://openalex.org/W7035948792","https://openalex.org/W2794033192","https://openalex.org/W3125890304","https://openalex.org/W2955460613","https://openalex.org/W201216448","https://openalex.org/W7000238984","https://openalex.org/W2740794714","https://openalex.org/W2775980283","https://openalex.org/W7096394728","https://openalex.org/W2411387771"],"abstract_inverted_index":{"The":[0,38],"bootstrap":[1,42,60],"algorithm":[2],"is":[3,22,25,48],"a":[4],"computational":[5],"intensive":[6],"procedure":[7],"to":[8,27,30,61],"derive":[9],"nonparametric":[10],"confidence":[11],"intervals":[12],"of":[13,40,58],"statistical":[14],"estimators":[15],"in":[16],"situations":[17],"where":[18],"an":[19],"analytic":[20],"solution":[21],"intractable.":[23],"It":[24],"ap-plied":[26],"neural":[28],"networks":[29],"estimate":[31],"the":[32,56,59],"predictive":[33],"distribution":[34],"for":[35],"unseen":[36],"inputs.":[37],"consistency":[39],"different":[41],"procedures":[43],"and":[44,64],"their":[45],"convergence":[46],"speed":[47],"discussed.":[49],"A":[50],"small":[51],"scale":[52],"simulation":[53],"experiment":[54],"shows":[55],"applicability":[57],"practical":[62],"problems":[63],"its":[65],"potential":[66],"use.":[67],"1":[68]},"counts_by_year":[{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":4},{"year":2020,"cited_by_count":2},{"year":2019,"cited_by_count":4},{"year":2018,"cited_by_count":1},{"year":2017,"cited_by_count":1}],"updated_date":"2026-11-08T12:12:28.789561","created_date":"2016-06-24T00:00:00"}