{"id":"https://openalex.org/W1538859348","doi":"https://doi.org/10.1109/ijcnn.2005.1556440","title":"On the evaluation of relevance learning by a multi-layer perceptron","display_name":"On the evaluation of relevance learning by a multi-layer perceptron","publication_year":2006,"publication_date":"2006-01-05","ids":{"openalex":"https://openalex.org/W1538859348","doi":"https://doi.org/10.1109/ijcnn.2005.1556440","mag":"1538859348"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn.2005.1556440","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2005.1556440","pdf_url":null,"source":{"id":"https://openalex.org/S4363609022","display_name":"Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005.","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005.","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/A5100618207","display_name":"Kenji Suzuki","orcid":"https://orcid.org/0000-0003-1736-5404"},"institutions":[{"id":"https://openalex.org/I146399215","display_name":"University of Tsukuba","ror":"https://ror.org/02956yf07","country_code":"JP","type":"education","lineage":["https://openalex.org/I146399215"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"K. Suzuki","raw_affiliation_strings":["Department of Intelligent Interaction Technologies, University of Tsukuba, Tsukuba, Japan"],"affiliations":[{"raw_affiliation_string":"Department of Intelligent Interaction Technologies, University of Tsukuba, Tsukuba, Japan","institution_ids":["https://openalex.org/I146399215"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5004364082","display_name":"Shuji Hashimoto","orcid":"https://orcid.org/0000-0003-2080-7795"},"institutions":[{"id":"https://openalex.org/I150744194","display_name":"Waseda University","ror":"https://ror.org/00ntfnx83","country_code":"JP","type":"education","lineage":["https://openalex.org/I150744194"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"S. Hashimoto","raw_affiliation_strings":["Department of Applied Physics, Waseda University, Tokyo, Japan"],"affiliations":[{"raw_affiliation_string":"Department of Applied Physics, Waseda University, Tokyo, Japan","institution_ids":["https://openalex.org/I150744194"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5100618207"],"corresponding_institution_ids":["https://openalex.org/I146399215"],"apc_list":null,"apc_paid":null,"fwci":1.1045,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.5890411,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"5","issue":null,"first_page":"3204","last_page":"3209"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10640","display_name":"Spectroscopy and Chemometric Analyses","score":0.977400004863739,"subfield":{"id":"https://openalex.org/subfields/1602","display_name":"Analytical Chemistry"},"field":{"id":"https://openalex.org/fields/16","display_name":"Chemistry"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10640","display_name":"Spectroscopy and Chemometric Analyses","score":0.977400004863739,"subfield":{"id":"https://openalex.org/subfields/1602","display_name":"Analytical Chemistry"},"field":{"id":"https://openalex.org/fields/16","display_name":"Chemistry"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11666","display_name":"Color Science and Applications","score":0.9638000130653381,"subfield":{"id":"https://openalex.org/subfields/3107","display_name":"Atomic and Molecular Physics, and Optics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.9585999846458435,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/perceptron","display_name":"Perceptron","score":0.7941340208053589},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7447689771652222},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6955133676528931},{"id":"https://openalex.org/keywords/relevance","display_name":"Relevance (law)","score":0.6823188662528992},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5061580538749695},{"id":"https://openalex.org/keywords/multilayer-perceptron","display_name":"Multilayer perceptron","score":0.5028459429740906},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.49446892738342285},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.48997464776039124},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4640575647354126},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4535742998123169},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.4327550530433655},{"id":"https://openalex.org/keywords/layer","display_name":"Layer (electronics)","score":0.43209171295166016},{"id":"https://openalex.org/keywords/multidimensional-scaling","display_name":"Multidimensional scaling","score":0.4234352111816406}],"concepts":[{"id":"https://openalex.org/C60908668","wikidata":"https://www.wikidata.org/wiki/Q690207","display_name":"Perceptron","level":3,"score":0.7941340208053589},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7447689771652222},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6955133676528931},{"id":"https://openalex.org/C158154518","wikidata":"https://www.wikidata.org/wiki/Q7310970","display_name":"Relevance (law)","level":2,"score":0.6823188662528992},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5061580538749695},{"id":"https://openalex.org/C179717631","wikidata":"https://www.wikidata.org/wiki/Q2991667","display_name":"Multilayer perceptron","level":3,"score":0.5028459429740906},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.49446892738342285},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.48997464776039124},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4640575647354126},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4535742998123169},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.4327550530433655},{"id":"https://openalex.org/C2779227376","wikidata":"https://www.wikidata.org/wiki/Q6505497","display_name":"Layer (electronics)","level":2,"score":0.43209171295166016},{"id":"https://openalex.org/C91682802","wikidata":"https://www.wikidata.org/wiki/Q620538","display_name":"Multidimensional scaling","level":2,"score":0.4234352111816406},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C178790620","wikidata":"https://www.wikidata.org/wiki/Q11351","display_name":"Organic chemistry","level":1,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"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/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn.2005.1556440","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2005.1556440","pdf_url":null,"source":{"id":"https://openalex.org/S4363609022","display_name":"Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005.","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005.","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":12,"referenced_works":["https://openalex.org/W1490549637","https://openalex.org/W1595334032","https://openalex.org/W1955396841","https://openalex.org/W1976364128","https://openalex.org/W2087087428","https://openalex.org/W2097383748","https://openalex.org/W2125947724","https://openalex.org/W2134312057","https://openalex.org/W2154642048","https://openalex.org/W2157379883","https://openalex.org/W2975800720","https://openalex.org/W4246354968"],"related_works":["https://openalex.org/W1993972960","https://openalex.org/W2077085168","https://openalex.org/W2166387735","https://openalex.org/W2096046065","https://openalex.org/W1975692209","https://openalex.org/W2056336673","https://openalex.org/W2191800066","https://openalex.org/W2029305120","https://openalex.org/W2062179223","https://openalex.org/W2461650107","https://openalex.org/W39507285","https://openalex.org/W2005382804","https://openalex.org/W2110378353","https://openalex.org/W3109485288","https://openalex.org/W1636004722","https://openalex.org/W2149772057","https://openalex.org/W2565956940","https://openalex.org/W2001929076","https://openalex.org/W2773071069","https://openalex.org/W2040592637"],"abstract_inverted_index":{"In":[0],"this":[1],"paper,":[2],"we":[3],"introduce":[4],"a":[5,12,40,45,58,74,78],"novel":[6],"method":[7,154],"of":[8,30,60,80,105,139,146,163,178],"relevance":[9,16,94],"learning":[10,17,21,34,48,129],"by":[11],"multi-layer":[13,42,53],"perceptron.":[14],"The":[15,33],"is":[18,37],"regarded":[19],"as":[20],"from":[22,57],"the":[23,31,51,66,69,87,92,96,103,106,121,127,136,144,161,175],"relationship":[24],"among":[25,98],"two":[26,81],"or":[27,82],"more":[28,83],"outputs":[29],"network.":[32],"network":[35,71,108,128],"architecture":[36],"based":[38],"on":[39,174],"simple":[41],"perceptron":[43,54],"with":[44,109,112,130,143],"modified":[46],"back-propagation":[47],"algorithm.":[49],"Unlike":[50],"conventional":[52],"that":[55],"learns":[56],"set":[59,79,182],"an":[61],"input":[62],"feature":[63],"vector":[64,84],"and":[65,86,123,151,183],"target":[67],"output,":[68],"proposed":[70,107],"can":[72],"obtain":[73],"nonlinear":[75],"mapping":[76],"between":[77],"inputs":[85],"desired":[88,93],"relevance.":[89],"For":[90],"instance,":[91],"represents":[95],"dissimilarity":[97],"given":[99],"objects.":[100],"We":[101,118,134],"show":[102],"performance":[104],"some":[110,131],"experiments":[111],"four":[113],"artificially":[114],"generated":[115],"data":[116,181],"set.":[117],"then":[119],"discuss":[120],"theoretical":[122],"mathematical":[124],"background":[125],"underlying":[126],"related":[132],"works.":[133],"evaluate":[135],"obtained":[137],"arrangement":[138],"objects":[140],"in":[141],"comparison":[142],"result":[145],"principle":[147],"component":[148],"analysis":[149],"(PCA)":[150],"multidimensional":[152,168],"scaling":[153],"(MDS).":[155],"This":[156],"work":[157],"also":[158],"contributes":[159],"to":[160],"measurement":[162],"human":[164],"subjective":[165],"evaluation":[166],"for":[167],"perceptual":[169],"scaling.":[170],"Some":[171],"experimental":[172],"results":[173],"low-dimensional":[176],"representation":[177],"color":[179],"hue":[180],"emotional":[184],"facial":[185],"images":[186],"were":[187],"presented.":[188]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
