{"id":"https://openalex.org/W2112978490","doi":"https://doi.org/10.1109/ijcnn.2007.4371223","title":"Multi-class kernel logistic regression: a fixed-size implementation","display_name":"Multi-class kernel logistic regression: a fixed-size implementation","publication_year":2007,"publication_date":"2007-08-01","ids":{"openalex":"https://openalex.org/W2112978490","doi":"https://doi.org/10.1109/ijcnn.2007.4371223","mag":"2112978490"},"language":"en","primary_location":{"is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2007.4371223","pdf_url":null,"source":{"id":"https://openalex.org/S4210195743","display_name":"IEEE International Conference on Neural Networks/IEEE ... International Conference on Neural Networks","issn_l":"1098-7576","issn":["1098-7576","1558-3902"],"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"conference"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false},"type":"article","type_crossref":"proceedings-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/A5031057978","display_name":"Peter Karsmakers","orcid":"https://orcid.org/0000-0001-8119-6823"},"institutions":[{"id":"https://openalex.org/I99464096","display_name":"KU Leuven","ror":"https://ror.org/05f950310","country_code":"BE","type":"education","lineage":["https://openalex.org/I99464096"]}],"countries":["BE"],"is_corresponding":false,"raw_author_name":"Peter Karsmakers","raw_affiliation_strings":["IIBT, K. H. Kempen Associatie, Geel, Belgium","IIBT, Katholieke Universiteit Leuven, Geel, Belgium"],"affiliations":[{"raw_affiliation_string":"IIBT, K. H. Kempen Associatie, Geel, Belgium","institution_ids":[]},{"raw_affiliation_string":"IIBT, Katholieke Universiteit Leuven, Geel, Belgium","institution_ids":["https://openalex.org/I99464096"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5074147384","display_name":"Kristiaan Pelckmans","orcid":"https://orcid.org/0000-0002-8486-0897"},"institutions":[{"id":"https://openalex.org/I99464096","display_name":"KU Leuven","ror":"https://ror.org/05f950310","country_code":"BE","type":"education","lineage":["https://openalex.org/I99464096"]}],"countries":["BE"],"is_corresponding":false,"raw_author_name":"Kristiaan Pelckmans","raw_affiliation_strings":["ESATSCD/SISTA, Katholieke Universiteit Leuven, Heverlee, Belgium"],"affiliations":[{"raw_affiliation_string":"ESATSCD/SISTA, Katholieke Universiteit Leuven, Heverlee, Belgium","institution_ids":["https://openalex.org/I99464096"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5078854904","display_name":"Johan A. K. Suykens","orcid":"https://orcid.org/0000-0002-8846-6352"},"institutions":[{"id":"https://openalex.org/I99464096","display_name":"KU Leuven","ror":"https://ror.org/05f950310","country_code":"BE","type":"education","lineage":["https://openalex.org/I99464096"]}],"countries":["BE"],"is_corresponding":false,"raw_author_name":"Johan A. K. Suykens","raw_affiliation_strings":["ESATSCD/SISTA, Katholieke Universiteit Leuven, Heverlee, Belgium"],"affiliations":[{"raw_affiliation_string":"ESATSCD/SISTA, Katholieke Universiteit Leuven, Heverlee, Belgium","institution_ids":["https://openalex.org/I99464096"]}]}],"institution_assertions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":2.881,"has_fulltext":true,"fulltext_origin":"ngrams","cited_by_count":50,"citation_normalized_percentile":{"value":0.97785,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":93,"max":94},"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"primary_topic":{"id":"https://openalex.org/T10057","display_name":"Face Recognition and Dimensionality Reduction Techniques","score":0.9981,"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"}},"topics":[{"id":"https://openalex.org/T10057","display_name":"Face Recognition and Dimensionality Reduction Techniques","score":0.9981,"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"}},{"id":"https://openalex.org/T10640","display_name":"Chemometrics in Analytical Chemistry and Food Technology","score":0.9971,"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/T10320","display_name":"Neural Network Fundamentals and Applications","score":0.995,"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/hessian-matrix","display_name":"Hessian matrix","score":0.79141665},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.5704148},{"id":"https://openalex.org/keywords/support-vector-machines","display_name":"Support Vector Machines","score":0.57002},{"id":"https://openalex.org/keywords/dimensionality-reduction","display_name":"Dimensionality Reduction","score":0.539377},{"id":"https://openalex.org/keywords/multivariate-calibration","display_name":"Multivariate Calibration","score":0.538019},{"id":"https://openalex.org/keywords/backpropagation-learning","display_name":"Backpropagation Learning","score":0.537997},{"id":"https://openalex.org/keywords/regularization","display_name":"Regularization (linguistics)","score":0.53158456},{"id":"https://openalex.org/keywords/non-negative-matrix-factorization","display_name":"Non-negative Matrix Factorization","score":0.520913}],"concepts":[{"id":"https://openalex.org/C203616005","wikidata":"https://www.wikidata.org/wiki/Q620495","display_name":"Hessian matrix","level":2,"score":0.79141665},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.6276729},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.5704148},{"id":"https://openalex.org/C145828037","wikidata":"https://www.wikidata.org/wiki/Q17086219","display_name":"Least squares support vector machine","level":3,"score":0.5604688},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.53158456},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.51664954},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.4902816},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.48105386},{"id":"https://openalex.org/C122280245","wikidata":"https://www.wikidata.org/wiki/Q620622","display_name":"Kernel method","level":3,"score":0.46544966},{"id":"https://openalex.org/C151956035","wikidata":"https://www.wikidata.org/wiki/Q1132755","display_name":"Logistic regression","level":2,"score":0.4247207},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.38162145},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3152207},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.2736178},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.17617595},{"id":"https://openalex.org/C118615104","wikidata":"https://www.wikidata.org/wiki/Q121416","display_name":"Discrete mathematics","level":1,"score":0.078332335}],"mesh":[],"locations_count":1,"locations":[{"is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2007.4371223","pdf_url":null,"source":{"id":"https://openalex.org/S4210195743","display_name":"IEEE International Conference on Neural Networks/IEEE ... International Conference on Neural Networks","issn_l":"1098-7576","issn":["1098-7576","1558-3902"],"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"conference"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false}],"best_oa_location":null,"sustainable_development_goals":[],"grants":[],"datasets":[],"versions":[],"referenced_works_count":18,"referenced_works":["https://openalex.org/W1496317909","https://openalex.org/W1596717185","https://openalex.org/W1978996791","https://openalex.org/W2015904350","https://openalex.org/W2027197817","https://openalex.org/W2070272652","https://openalex.org/W2084812512","https://openalex.org/W2109816097","https://openalex.org/W2112545207","https://openalex.org/W2114229504","https://openalex.org/W2140586694","https://openalex.org/W2141915722","https://openalex.org/W2151499551","https://openalex.org/W2153635508","https://openalex.org/W2164078599","https://openalex.org/W2982720039","https://openalex.org/W3029645440","https://openalex.org/W4293775970"],"related_works":["https://openalex.org/W4386075310","https://openalex.org/W4385064145","https://openalex.org/W2913522741","https://openalex.org/W2800988248","https://openalex.org/W2169565408","https://openalex.org/W2169520161","https://openalex.org/W2127229869","https://openalex.org/W2095626363","https://openalex.org/W2089892314","https://openalex.org/W1603091392"],"abstract_inverted_index":{"This":[0],"research":[1],"studies":[2],"a":[3,34,58,126,134,142],"practical":[4],"iterative":[5],"algorithm":[6],"for":[7],"multi-class":[8,89,135],"kernel":[9,136],"logistic":[10,137],"regression":[11,138],"(KLR).":[12],"Starting":[13],"from":[14],"the":[15,24,51,66,95,100,103,116],"negative":[16],"penalized":[17],"log":[18],"likelihood":[19],"criterium":[20],"we":[21],"show":[22],"that":[23,50,119],"optimization":[25],"problem":[26],"in":[27,61,94,125],"each":[28,62],"iteration":[29],"can":[30,121],"be":[31,122],"solved":[32],"by":[33],"weighted":[35],"version":[36,108],"of":[37,102,109],"least":[38],"squares":[39],"support":[40],"vector":[41],"machines":[42],"(LS-SVMs).":[43],"In":[44,65],"this":[45,83],"derivation":[46],"it":[47,120],"turns":[48],"out":[49],"global":[52],"regularization":[53,60],"term":[54],"is":[55,71,112,131],"reflected":[56],"as":[57],"usual":[59],"separate":[63],"step.":[64],"LS-SVM":[67,70],"framework,":[68],"fixed-size":[69],"known":[72],"to":[73,85,141],"perform":[74],"well":[75],"on":[76],"large":[77,87],"data":[78],"sets.":[79],"We":[80],"therefore":[81],"implement":[82],"model":[84,139],"solve":[86],"scale":[88],"KLR":[90],"problems":[91],"with":[92],"estimation":[93],"primal":[96],"space.":[97],"To":[98],"reduce":[99],"size":[101],"Hessian,":[104],"an":[105],"alternating":[106],"descent":[107],"Newton's":[110],"method":[111],"used":[113,124],"which":[114],"has":[115],"extra":[117],"advantage":[118],"easily":[123],"distributed":[127],"computing":[128],"environment.":[129],"It":[130],"investigated":[132],"how":[133],"compares":[140],"one-versus-all":[143],"coding":[144],"scheme.":[145]},"cited_by_api_url":"https://api.openalex.org/works?filter=cites:W2112978490","counts_by_year":[{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":5},{"year":2021,"cited_by_count":6},{"year":2020,"cited_by_count":3},{"year":2018,"cited_by_count":1},{"year":2017,"cited_by_count":2},{"year":2016,"cited_by_count":3},{"year":2015,"cited_by_count":2},{"year":2014,"cited_by_count":2},{"year":2013,"cited_by_count":1},{"year":2012,"cited_by_count":3}],"updated_date":"2024-12-03T05:58:03.814799","created_date":"2016-06-24"}