{"id":"https://openalex.org/W1979662673","doi":"https://doi.org/10.1109/ijcnn.2014.6889665","title":"An analysis based on F-discrepancy for sampling in regression tree learning","display_name":"An analysis based on F-discrepancy for sampling in regression tree learning","publication_year":2014,"publication_date":"2014-07-01","ids":{"openalex":"https://openalex.org/W1979662673","doi":"https://doi.org/10.1109/ijcnn.2014.6889665","mag":"1979662673"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn.2014.6889665","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2014.6889665","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2014 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-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/A5043950568","display_name":"Cristiano Cervellera","orcid":"https://orcid.org/0000-0003-2017-0448"},"institutions":[{"id":"https://openalex.org/I197604219","display_name":"National Academies of Sciences, Engineering, and Medicine","ror":"https://ror.org/02eq2w707","country_code":"US","type":"government","lineage":["https://openalex.org/I197604219"]},{"id":"https://openalex.org/I4210146308","display_name":"Institute of Intelligent Systems for Automation","ror":"https://ror.org/051t1q308","country_code":"IT","type":"facility","lineage":["https://openalex.org/I4210146308","https://openalex.org/I4210155236"]},{"id":"https://openalex.org/I4210155236","display_name":"National Research Council","ror":"https://ror.org/04zaypm56","country_code":"IT","type":"funder","lineage":["https://openalex.org/I4210155236"]}],"countries":["IT","US"],"is_corresponding":true,"raw_author_name":"Cristiano Cervellera","raw_affiliation_strings":["National Research Council, Institute of Intelligent Systems for Automation, Genova, Italy","Nat. Res. Council, Inst. of Intell. Syst. for Autom., Genoa, Italy"],"affiliations":[{"raw_affiliation_string":"National Research Council, Institute of Intelligent Systems for Automation, Genova, Italy","institution_ids":["https://openalex.org/I4210146308","https://openalex.org/I4210155236"]},{"raw_affiliation_string":"Nat. Res. Council, Inst. of Intell. Syst. for Autom., Genoa, Italy","institution_ids":["https://openalex.org/I197604219"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5017942383","display_name":"Mauro Gaggero","orcid":"https://orcid.org/0000-0002-5048-4141"},"institutions":[{"id":"https://openalex.org/I4210146308","display_name":"Institute of Intelligent Systems for Automation","ror":"https://ror.org/051t1q308","country_code":"IT","type":"facility","lineage":["https://openalex.org/I4210146308","https://openalex.org/I4210155236"]},{"id":"https://openalex.org/I197604219","display_name":"National Academies of Sciences, Engineering, and Medicine","ror":"https://ror.org/02eq2w707","country_code":"US","type":"government","lineage":["https://openalex.org/I197604219"]},{"id":"https://openalex.org/I4210155236","display_name":"National Research Council","ror":"https://ror.org/04zaypm56","country_code":"IT","type":"funder","lineage":["https://openalex.org/I4210155236"]}],"countries":["IT","US"],"is_corresponding":false,"raw_author_name":"Mauro Gaggero","raw_affiliation_strings":["National Research Council, Institute of Intelligent Systems for Automation, Genova, Italy","Nat. Res. Council, Inst. of Intell. Syst. for Autom., Genoa, Italy"],"affiliations":[{"raw_affiliation_string":"National Research Council, Institute of Intelligent Systems for Automation, Genova, Italy","institution_ids":["https://openalex.org/I4210146308","https://openalex.org/I4210155236"]},{"raw_affiliation_string":"Nat. Res. Council, Inst. of Intell. Syst. for Autom., Genoa, Italy","institution_ids":["https://openalex.org/I197604219"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5050914225","display_name":"Danilo Macci\u00f2","orcid":"https://orcid.org/0000-0002-2627-4953"},"institutions":[{"id":"https://openalex.org/I4210146308","display_name":"Institute of Intelligent Systems for Automation","ror":"https://ror.org/051t1q308","country_code":"IT","type":"facility","lineage":["https://openalex.org/I4210146308","https://openalex.org/I4210155236"]},{"id":"https://openalex.org/I197604219","display_name":"National Academies of Sciences, Engineering, and Medicine","ror":"https://ror.org/02eq2w707","country_code":"US","type":"government","lineage":["https://openalex.org/I197604219"]},{"id":"https://openalex.org/I4210155236","display_name":"National Research Council","ror":"https://ror.org/04zaypm56","country_code":"IT","type":"funder","lineage":["https://openalex.org/I4210155236"]}],"countries":["IT","US"],"is_corresponding":false,"raw_author_name":"Danilo Maccio","raw_affiliation_strings":["National Research Council, Institute of Intelligent Systems for Automation, Genova, Italy","Nat. Res. Council, Inst. of Intell. Syst. for Autom., Genoa, Italy"],"affiliations":[{"raw_affiliation_string":"National Research Council, Institute of Intelligent Systems for Automation, Genova, Italy","institution_ids":["https://openalex.org/I4210146308","https://openalex.org/I4210155236"]},{"raw_affiliation_string":"Nat. Res. Council, Inst. of Intell. Syst. for Autom., Genoa, Italy","institution_ids":["https://openalex.org/I197604219"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5043950568"],"corresponding_institution_ids":["https://openalex.org/I197604219","https://openalex.org/I4210146308","https://openalex.org/I4210155236"],"apc_list":null,"apc_paid":null,"fwci":0.9767,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.76063326,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"1115","last_page":"1121"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10136","display_name":"Statistical Methods and Inference","score":0.9979000091552734,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10136","display_name":"Statistical Methods and Inference","score":0.9979000091552734,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12072","display_name":"Machine Learning and Algorithms","score":0.994700014591217,"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":0.9919000267982483,"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/estimator","display_name":"Estimator","score":0.6856558322906494},{"id":"https://openalex.org/keywords/piecewise","display_name":"Piecewise","score":0.5774700045585632},{"id":"https://openalex.org/keywords/tree","display_name":"Tree (set theory)","score":0.5511910915374756},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5301697254180908},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.4634641706943512},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4515674114227295},{"id":"https://openalex.org/keywords/constructive","display_name":"Constructive","score":0.43990784883499146},{"id":"https://openalex.org/keywords/regression-analysis","display_name":"Regression analysis","score":0.43223050236701965},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.3675127625465393},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3649519383907318},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.2594515085220337},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.2212841510772705},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.18190577626228333}],"concepts":[{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.6856558322906494},{"id":"https://openalex.org/C164660894","wikidata":"https://www.wikidata.org/wiki/Q2037833","display_name":"Piecewise","level":2,"score":0.5774700045585632},{"id":"https://openalex.org/C113174947","wikidata":"https://www.wikidata.org/wiki/Q2859736","display_name":"Tree (set theory)","level":2,"score":0.5511910915374756},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5301697254180908},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4634641706943512},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4515674114227295},{"id":"https://openalex.org/C2778701210","wikidata":"https://www.wikidata.org/wiki/Q28130034","display_name":"Constructive","level":3,"score":0.43990784883499146},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.43223050236701965},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.3675127625465393},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3649519383907318},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.2594515085220337},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.2212841510772705},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.18190577626228333},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn.2014.6889665","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2014.6889665","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2014 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":16,"referenced_works":["https://openalex.org/W181475723","https://openalex.org/W1480376833","https://openalex.org/W1594031697","https://openalex.org/W1967830305","https://openalex.org/W1983585926","https://openalex.org/W1984208821","https://openalex.org/W2022188138","https://openalex.org/W2097394825","https://openalex.org/W2120103453","https://openalex.org/W2151566653","https://openalex.org/W2787894218","https://openalex.org/W3120740533","https://openalex.org/W4210909502","https://openalex.org/W4238205818","https://openalex.org/W4300580367","https://openalex.org/W6646182838"],"related_works":["https://openalex.org/W4311883357","https://openalex.org/W2377770720","https://openalex.org/W2113626999","https://openalex.org/W4287880334","https://openalex.org/W4366700029","https://openalex.org/W160889949","https://openalex.org/W2364696089","https://openalex.org/W3175363083","https://openalex.org/W309910862","https://openalex.org/W4210637712"],"abstract_inverted_index":{"When":[0],"the":[1,14,17,28,31,44,50,64,76,79,86,97,105,111,126,129,141,146,168],"problem":[2,169],"of":[3,16,30,78,88,99,101,104,113,123],"learning":[4],"from":[5,96],"data":[6,181],"is":[7,20,40,94,133],"solved":[8],"through":[9],"a":[10,60,68,82,115,121,156,175,179],"regression":[11,92],"tree":[12,51,65],"estimator,":[13],"quality":[15],"available":[18,106],"observations":[19],"an":[21],"important":[22],"issue,":[23],"since":[24],"it":[25],"influences":[26],"directly":[27],"accuracy":[29,87],"resulting":[32],"model.":[33],"It":[34],"becomes":[35],"particuarly":[36],"relevant":[37],"when":[38,55],"there":[39],"freedom":[41],"to":[42,48,58,62,74,135,140,151,160],"sample":[43],"input":[45,71,107,130,165],"space":[46],"arbitrarily":[47],"build":[49],"model":[52],"or,":[53],"alternatively,":[54],"we":[56,149],"need":[57],"select":[59,161],"subsample":[61],"train":[63],"estimator":[66,144],"on":[67,81,91],"computationally":[69],"feasible":[70],"set,":[72],"or":[73],"evaluate":[75],"goodness":[77],"estimation":[80,89],"test":[83],"set.":[84,182],"Here":[85],"based":[90],"trees":[93],"analyzed":[95],"point":[98],"view":[100],"geometric":[102],"properties":[103],"data.":[108],"In":[109],"particular,":[110],"concept":[112],"F-discrepancy,":[114],"quantity":[116],"that":[117],"measures":[118],"how":[119],"well":[120],"set":[122],"points":[124],"represents":[125],"distribution":[127],"underlying":[128],"generation":[131],"process,":[132],"applied":[134],"derive":[136],"conditions":[137],"for":[138,145,167],"convergence":[139],"optimal":[142],"piecewise-constant":[143],"unknown":[147],"function":[148],"want":[150],"learn.":[152],"The":[153],"analysis":[154],"has":[155],"constructive":[157],"nature,":[158],"allowing":[159],"in":[162,174],"practice":[163],"good":[164],"sets":[166],"at":[170],"hand,":[171],"as":[172],"shown":[173],"simulation":[176],"example":[177],"involving":[178],"real":[180]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2017,"cited_by_count":1},{"year":2015,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}