{"id":"https://openalex.org/W2964153600","doi":"https://doi.org/10.1109/icpr.2016.7899802","title":"Learning opposites using neural networks","display_name":"Learning opposites using neural networks","publication_year":2016,"publication_date":"2016-12-01","ids":{"openalex":"https://openalex.org/W2964153600","doi":"https://doi.org/10.1109/icpr.2016.7899802","mag":"2964153600"},"language":"en","primary_location":{"id":"doi:10.1109/icpr.2016.7899802","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icpr.2016.7899802","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 23rd International Conference on Pattern Recognition (ICPR)","raw_type":"proceedings-article"},"type":"preprint","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/A5041584243","display_name":"Shivam Kalra","orcid":"https://orcid.org/0009-0003-6804-8908"},"institutions":[{"id":"https://openalex.org/I151746483","display_name":"University of Waterloo","ror":"https://ror.org/01aff2v68","country_code":"CA","type":"education","lineage":["https://openalex.org/I151746483"]}],"countries":["CA"],"is_corresponding":true,"raw_author_name":"Shivam Kalra","raw_affiliation_strings":["KIMIA Lab, University of Waterloo, Canada"],"affiliations":[{"raw_affiliation_string":"KIMIA Lab, University of Waterloo, Canada","institution_ids":["https://openalex.org/I151746483"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102891991","display_name":"Aditya Sriram","orcid":null},"institutions":[{"id":"https://openalex.org/I151746483","display_name":"University of Waterloo","ror":"https://ror.org/01aff2v68","country_code":"CA","type":"education","lineage":["https://openalex.org/I151746483"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Aditya Sriram","raw_affiliation_strings":["KIMIA Lab, University of Waterloo, Canada"],"affiliations":[{"raw_affiliation_string":"KIMIA Lab, University of Waterloo, Canada","institution_ids":["https://openalex.org/I151746483"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5077727968","display_name":"Shahryar Rahnamayan","orcid":"https://orcid.org/0000-0002-6990-787X"},"institutions":[{"id":"https://openalex.org/I39470171","display_name":"University of Ontario Institute of Technology","ror":"https://ror.org/016zre027","country_code":"CA","type":"education","lineage":["https://openalex.org/I39470171"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Shahryar Rahnamayan","raw_affiliation_strings":["Elect., Comp. & Software Eng., University of Ontario Institute of Technology, Canada"],"affiliations":[{"raw_affiliation_string":"Elect., Comp. & Software Eng., University of Ontario Institute of Technology, Canada","institution_ids":["https://openalex.org/I39470171"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5038566332","display_name":"Hamid R. Tizhoosh","orcid":"https://orcid.org/0000-0001-5488-601X"},"institutions":[{"id":"https://openalex.org/I151746483","display_name":"University of Waterloo","ror":"https://ror.org/01aff2v68","country_code":"CA","type":"education","lineage":["https://openalex.org/I151746483"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"H.R. Tizhoosh","raw_affiliation_strings":["KIMIA Lab, University of Waterloo, Canada"],"affiliations":[{"raw_affiliation_string":"KIMIA Lab, University of Waterloo, Canada","institution_ids":["https://openalex.org/I151746483"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5041584243"],"corresponding_institution_ids":["https://openalex.org/I151746483"],"apc_list":null,"apc_paid":null,"fwci":1.12750752,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.91810267,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"1213","last_page":"1218"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.98089998960495,"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":0.98089998960495,"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/T10820","display_name":"Fuzzy Logic and Control Systems","score":0.9355999827384949,"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/computer-science","display_name":"Computer science","score":0.6608643531799316},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.6150973439216614},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4630328416824341}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6608643531799316},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6150973439216614},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4630328416824341}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icpr.2016.7899802","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icpr.2016.7899802","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 23rd International Conference on Pattern Recognition (ICPR)","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":25,"referenced_works":["https://openalex.org/W190427941","https://openalex.org/W1887884901","https://openalex.org/W1987012246","https://openalex.org/W2003521715","https://openalex.org/W2019884884","https://openalex.org/W2043846600","https://openalex.org/W2050741788","https://openalex.org/W2069209396","https://openalex.org/W2070283188","https://openalex.org/W2070815834","https://openalex.org/W2081239738","https://openalex.org/W2084993914","https://openalex.org/W2085583853","https://openalex.org/W2092682490","https://openalex.org/W2105990447","https://openalex.org/W2107722300","https://openalex.org/W2113517874","https://openalex.org/W2123682012","https://openalex.org/W2154369526","https://openalex.org/W2155801811","https://openalex.org/W2162745921","https://openalex.org/W2166468351","https://openalex.org/W2169384954","https://openalex.org/W2171253128","https://openalex.org/W6676797890"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W4396696052","https://openalex.org/W4402327032","https://openalex.org/W2382290278"],"abstract_inverted_index":{"Many":[0],"research":[1],"works":[2],"have":[3,25,80,193],"successfully":[4],"extended":[5],"algorithms":[6,240],"such":[7],"as":[8],"evolutionary":[9],"algorithms,":[10],"reinforcement":[11],"agents":[12],"and":[13,33,41,78,90,157,175,188],"neural":[14,163,223],"networks":[15,164],"using":[16,160,197],"\"opposition-based":[17],"learning\"":[18],"(OBL).":[19],"Two":[20],"types":[21],"of":[22,73,111,115,122,221],"the":[23,29,44,52,58,61,109,116,120,126,139,154,161,172,178,183,218,227],"\"opposites\"":[24],"been":[26,212],"defined":[27],"in":[28,39,60,85,93,107,113,245],"literature,":[30],"namely":[31],"type-I":[32,66],"type-II.":[34],"The":[35,215],"former":[36],"are":[37,68,102],"linear":[38,82],"nature":[40],"applicable":[42],"to":[43,49,104,132,137,149,181,201,225],"variable":[45],"space,":[46],"hence":[47],"easy":[48],"calculate.":[50],"On":[51],"other":[53],"hand,":[54,129],"type-II":[55,74,100,140,151],"opposites":[56,67,75,101,152],"capture":[57],"\"oppositeness\"":[59],"output":[62],"space.":[63],"In":[64,119,142],"fact,":[65],"considered":[69],"a":[70,81,96,222,242],"special":[71],"case":[72],"where":[76],"inputs":[77,89,156],"outputs":[79,91,159],"relationship.":[83,98],"However,":[84],"many":[86],"real-world":[87],"problems,":[88],"do":[92],"fact":[94],"exhibit":[95],"nonlinear":[97],"Therefore,":[99],"expected":[103],"be":[105,133],"better":[106,219],"capturing":[108],"sense":[110],"\"opposition\"":[112],"terms":[114],"input-output":[117],"relation.":[118],"absence":[121],"any":[123],"knowledge":[124],"about":[125],"problem":[127],"at":[128],"there":[130],"seems":[131],"no":[134],"intuitive":[135],"way":[136],"calculate":[138],"opposites.":[141,228],"this":[143],"paper,":[144],"we":[145],"introduce":[146],"an":[147,205],"approach":[148,209,224],"learn":[150,182,226],"from":[153],"given":[155],"their":[158],"artificial":[162],"(ANNs).":[165],"We":[166,192],"first":[167],"perform":[168],"opposition":[169],"mining":[170],"on":[171],"sample":[173],"data,":[174],"then":[176],"use":[177],"mined":[179],"data":[180],"relationship":[184],"between":[185],"input":[186],"x":[187],"its":[189],"opposite":[190],"x\u030c.":[191],"validated":[194],"our":[195],"algorithm":[196],"various":[198],"benchmark":[199],"functions":[200],"compare":[202],"it":[203],"against":[204],"evolving":[206],"fuzzy":[207],"inference":[208],"that":[210],"has":[211],"recently":[213],"introduced.":[214],"results":[216],"show":[217],"performance":[220],"This":[229],"will":[230],"create":[231],"new":[232],"possibilities":[233],"for":[234],"integrating":[235],"oppositional":[236],"schemes":[237],"within":[238],"existing":[239],"promising":[241],"potential":[243],"increase":[244],"convergence":[246],"speed":[247],"and/or":[248],"accuracy.":[249]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2019,"cited_by_count":2},{"year":2018,"cited_by_count":1},{"year":2017,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}