{"id":"https://openalex.org/W2611432532","doi":"https://doi.org/10.1145/1276958.1277289","title":"Learning noise","display_name":"Learning noise","publication_year":2007,"publication_date":"2007-07-07","ids":{"openalex":"https://openalex.org/W2611432532","doi":"https://doi.org/10.1145/1276958.1277289","mag":"2611432532"},"language":"en","primary_location":{"id":"doi:10.1145/1276958.1277289","is_oa":false,"landing_page_url":"https://doi.org/10.1145/1276958.1277289","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 9th annual conference on Genetic and evolutionary computation","raw_type":"proceedings-article"},"type":"conference-paper","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/A5088013683","display_name":"Michael D. Schmidt","orcid":"https://orcid.org/0000-0002-8062-4491"},"institutions":[{"id":"https://openalex.org/I205783295","display_name":"Cornell University","ror":"https://ror.org/05bnh6r87","country_code":"US","type":"education","lineage":["https://openalex.org/I205783295"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Michael D. Schmidt","raw_affiliation_strings":["Cornell University, Ithaca, NY"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Cornell University, Ithaca, NY","institution_ids":["https://openalex.org/I205783295"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5025894735","display_name":"Hod Lipson","orcid":"https://orcid.org/0000-0003-0769-4618"},"institutions":[{"id":"https://openalex.org/I205783295","display_name":"Cornell University","ror":"https://ror.org/05bnh6r87","country_code":"US","type":"education","lineage":["https://openalex.org/I205783295"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Hod Lipson","raw_affiliation_strings":["Cornell University, Ithaca, NY"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Cornell University, Ithaca, NY","institution_ids":["https://openalex.org/I205783295"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I205783295"],"apc_list":null,"apc_paid":null,"fwci":0.4451,"has_fulltext":false,"cited_by_count":16,"citation_normalized_percentile":{"value":0.62994715,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"1680","last_page":"1685"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11975","display_name":"Evolutionary Algorithms and Applications","score":1.0,"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/T11975","display_name":"Evolutionary Algorithms and Applications","score":1.0,"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/T10100","display_name":"Metaheuristic Optimization Algorithms Research","score":0.9984999895095825,"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/T10848","display_name":"Advanced Multi-Objective Optimization Algorithms","score":0.9785000085830688,"subfield":{"id":"https://openalex.org/subfields/1703","display_name":"Computational Theory and Mathematics"},"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/computer-science","display_name":"Computer science","score":0.6347723603248596},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.42758601903915405},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.31779128313064575}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6347723603248596},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.42758601903915405},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.31779128313064575},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/1276958.1277289","is_oa":false,"landing_page_url":"https://doi.org/10.1145/1276958.1277289","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 9th annual conference on Genetic and evolutionary computation","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Responsible consumption and production","id":"https://metadata.un.org/sdg/12","score":0.41999998688697815}],"awards":[],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320337391","display_name":"Division of Civil, Mechanical and Manufacturing Innovation","ror":"https://ror.org/028yd4c30"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":18,"referenced_works":["https://openalex.org/W1550570395","https://openalex.org/W1576818901","https://openalex.org/W1854202107","https://openalex.org/W2036426733","https://openalex.org/W2053426042","https://openalex.org/W2106644119","https://openalex.org/W2129202132","https://openalex.org/W2129249398","https://openalex.org/W2138645843","https://openalex.org/W2152382764","https://openalex.org/W2163750589","https://openalex.org/W2237800080","https://openalex.org/W2498631646","https://openalex.org/W3150248551","https://openalex.org/W4299326032","https://openalex.org/W6606390901","https://openalex.org/W6675903431","https://openalex.org/W6679149748"],"related_works":["https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W2358668433","https://openalex.org/W2376932109","https://openalex.org/W2382290278","https://openalex.org/W2350741829","https://openalex.org/W2130043461","https://openalex.org/W2530322880","https://openalex.org/W1596801655"],"abstract_inverted_index":{"In":[0],"this":[1,43,68],"paper":[2],"we":[3],"propose":[4],"a":[5,89,96,103,137,146],"genetic":[6],"programming":[7],"approach":[8,69],"to":[9,21,111,140,169],"learning":[10,18,76],"stochastic":[11,90,93,158,175],"models":[12,127,173],"with":[13,49],"unsymmetrical":[14],"noise":[15,38,51,62,82,110,151],"distributions.":[16],"Most":[17],"algorithms":[19],"try":[20],"learn":[22],"from":[23],"noisy":[24],"data":[25,48],"by":[26,80,148],"modeling":[27],"the":[28,86,112,142,154,163,167],"maximum":[29],"likelihood":[30],"output":[31,121],"or":[32,99],"least":[33],"squared":[34],"error,":[35],"assuming":[36],"that":[37,106],"effects":[39],"average":[40],"out.":[41],"While":[42],"process":[44],"works":[45],"well":[46],"for":[47],"symmetrical":[50],"distributions":[52],"(such":[53],"as":[54,88],"Gaussian":[55],"observation":[56],"noise),":[57],"many":[58],"real-life":[59],"sources":[60,83],"of":[61,102,125,145],"are":[63,177],"not":[64,71],"symmetrically":[65],"distributed,":[66],"thus":[67],"does":[70],"hold.":[72],"We":[73,135],"suggest":[74],"improved":[75],"can":[77,107,117],"be":[78],"obtained":[79],"including":[81],"explicitly":[84],"in":[85],"model":[87,144],"element.":[91],"A":[92],"element":[94],"is":[95],"random":[97],"sub-process":[98],"latent":[100],"variable":[101],"hidden":[104,183],"system":[105,147],"propagate":[108],"nonlinear":[109,180],"observable":[113],"outputs.":[114],"Stochastic":[115],"elements":[116,159,176],"skew":[118],"and":[119,130,181],"distort":[120],"features":[122],"making":[123],"regression":[124],"analytical":[126,143,172],"particularly":[128],"difficult":[129],"error":[131],"minimizing":[132],"approaches":[133],"inhibiting.":[134],"introduce":[136],"new":[138],"method":[139],"infer":[141],"decomposing":[149],"non-uniform":[150],"observed":[152],"at":[153],"outputs":[155],"into":[156],"uniform":[157],"appearing":[160],"symbolically":[161],"inside":[162,179],"system.":[164],"Results":[165],"demonstrate":[166],"ability":[168],"regress":[170],"exact":[171],"where":[174],"embedded":[178],"polynomial":[182],"systems.":[184]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":3},{"year":2017,"cited_by_count":1},{"year":2016,"cited_by_count":1},{"year":2012,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2017-05-12T00:00:00"}
