{"id":"https://openalex.org/W2068965541","doi":"https://doi.org/10.1080/03610910902940150","title":"Nonlinear Logistic Discrimination Via Regularized Gaussian Basis Expansions","display_name":"Nonlinear Logistic Discrimination Via Regularized Gaussian Basis Expansions","publication_year":2009,"publication_date":"2009-05-21","ids":{"openalex":"https://openalex.org/W2068965541","doi":"https://doi.org/10.1080/03610910902940150","mag":"2068965541"},"language":"en","primary_location":{"id":"doi:10.1080/03610910902940150","is_oa":false,"landing_page_url":"https://doi.org/10.1080/03610910902940150","pdf_url":null,"source":{"id":"https://openalex.org/S153329750","display_name":"Communications in Statistics - Simulation and Computation","issn_l":"0361-0918","issn":["0361-0918","1532-4141"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Communications in Statistics - Simulation and Computation","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"http://hdl.handle.net/2324/13216","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5025934775","display_name":"Shuichi Kawano","orcid":"https://orcid.org/0000-0002-0804-0141"},"institutions":[{"id":"https://openalex.org/I135598925","display_name":"Kyushu University","ror":"https://ror.org/00p4k0j84","country_code":"JP","type":"education","lineage":["https://openalex.org/I135598925"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Shuichi Kawano","raw_affiliation_strings":["Faculty of Mathematics , Kyushu University , Fukuoka , Japan","Faculty of Mathematics Kyushu University  Fukuoka Japan"],"affiliations":[{"raw_affiliation_string":"Faculty of Mathematics , Kyushu University , Fukuoka , Japan","institution_ids":["https://openalex.org/I135598925"]},{"raw_affiliation_string":"Faculty of Mathematics Kyushu University  Fukuoka Japan","institution_ids":["https://openalex.org/I135598925"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5102060553","display_name":"Sadanori Konishi","orcid":null},"institutions":[{"id":"https://openalex.org/I135598925","display_name":"Kyushu University","ror":"https://ror.org/00p4k0j84","country_code":"JP","type":"education","lineage":["https://openalex.org/I135598925"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Sadanori Konishi","raw_affiliation_strings":["Faculty of Mathematics , Kyushu University , Fukuoka , Japan","Faculty of Mathematics Kyushu University  Fukuoka Japan"],"affiliations":[{"raw_affiliation_string":"Faculty of Mathematics , Kyushu University , Fukuoka , Japan","institution_ids":["https://openalex.org/I135598925"]},{"raw_affiliation_string":"Faculty of Mathematics Kyushu University  Fukuoka Japan","institution_ids":["https://openalex.org/I135598925"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5025934775"],"corresponding_institution_ids":["https://openalex.org/I135598925"],"apc_list":null,"apc_paid":null,"fwci":2.2944,"has_fulltext":true,"cited_by_count":15,"citation_normalized_percentile":{"value":0.86529423,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":97},"biblio":{"volume":"38","issue":"7","first_page":"1414","last_page":"1425"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10640","display_name":"Spectroscopy and Chemometric Analyses","score":0.986299991607666,"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":0.986299991607666,"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/T10876","display_name":"Fault Detection and Control Systems","score":0.9753999710083008,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.9682000279426575,"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/nonlinear-system","display_name":"Nonlinear system","score":0.6849995255470276},{"id":"https://openalex.org/keywords/basis","display_name":"Basis (linear algebra)","score":0.6586602926254272},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.6308037042617798},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.6204992532730103},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6155456304550171},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5929893255233765},{"id":"https://openalex.org/keywords/linear-discriminant-analysis","display_name":"Linear discriminant analysis","score":0.5715232491493225},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.543877899646759},{"id":"https://openalex.org/keywords/discriminant","display_name":"Discriminant","score":0.5241478085517883},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5172348618507385},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.49956202507019043},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4700701832771301},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.4694995880126953},{"id":"https://openalex.org/keywords/logistic-regression","display_name":"Logistic regression","score":0.4371776282787323},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.40827125310897827},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3975692391395569},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.3341764211654663}],"concepts":[{"id":"https://openalex.org/C158622935","wikidata":"https://www.wikidata.org/wiki/Q660848","display_name":"Nonlinear system","level":2,"score":0.6849995255470276},{"id":"https://openalex.org/C12426560","wikidata":"https://www.wikidata.org/wiki/Q189569","display_name":"Basis (linear algebra)","level":2,"score":0.6586602926254272},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.6308037042617798},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.6204992532730103},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6155456304550171},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5929893255233765},{"id":"https://openalex.org/C69738355","wikidata":"https://www.wikidata.org/wiki/Q1228929","display_name":"Linear discriminant analysis","level":2,"score":0.5715232491493225},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.543877899646759},{"id":"https://openalex.org/C78397625","wikidata":"https://www.wikidata.org/wiki/Q192487","display_name":"Discriminant","level":2,"score":0.5241478085517883},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5172348618507385},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.49956202507019043},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4700701832771301},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.4694995880126953},{"id":"https://openalex.org/C151956035","wikidata":"https://www.wikidata.org/wiki/Q1132755","display_name":"Logistic regression","level":2,"score":0.4371776282787323},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.40827125310897827},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3975692391395569},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.3341764211654663},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1080/03610910902940150","is_oa":false,"landing_page_url":"https://doi.org/10.1080/03610910902940150","pdf_url":null,"source":{"id":"https://openalex.org/S153329750","display_name":"Communications in Statistics - Simulation and Computation","issn_l":"0361-0918","issn":["0361-0918","1532-4141"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Communications in Statistics - Simulation and Computation","raw_type":"journal-article"},{"id":"pmh:oai:catalog.lib.kyushu-u.ac.jp:2324/13216","is_oa":true,"landing_page_url":"http://hdl.handle.net/2324/13216","pdf_url":null,"source":{"id":"https://openalex.org/S4306400832","display_name":"Kyushu University Institutional Repository (QIR) (Kyushu University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I135598925","host_organization_name":"Kyushu University","host_organization_lineage":["https://openalex.org/I135598925"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Communications in Statistics : Simulation and Computation","raw_type":"journal article"},{"id":"pmh:oai:irdb.nii.ac.jp:01211:0005973673","is_oa":true,"landing_page_url":"https://hdl.handle.net/2324/13216","pdf_url":null,"source":{"id":"https://openalex.org/S7407056385","display_name":"Institutional Repositories DataBase (IRDB)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I184597095","host_organization_name":"National Institute of Informatics","host_organization_lineage":["https://openalex.org/I184597095"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Communications in Statistics : Simulation and Computation","raw_type":"journal article"}],"best_oa_location":{"id":"pmh:oai:catalog.lib.kyushu-u.ac.jp:2324/13216","is_oa":true,"landing_page_url":"http://hdl.handle.net/2324/13216","pdf_url":null,"source":{"id":"https://openalex.org/S4306400832","display_name":"Kyushu University Institutional Repository (QIR) (Kyushu University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I135598925","host_organization_name":"Kyushu University","host_organization_lineage":["https://openalex.org/I135598925"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Communications in Statistics : Simulation and Computation","raw_type":"journal article"},"sustainable_development_goals":[{"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10","score":0.6399999856948853}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":43,"referenced_works":["https://openalex.org/W188554010","https://openalex.org/W1480376833","https://openalex.org/W1501760102","https://openalex.org/W1515438724","https://openalex.org/W1526930689","https://openalex.org/W1601795611","https://openalex.org/W1649056071","https://openalex.org/W1663973292","https://openalex.org/W1679913846","https://openalex.org/W1775809622","https://openalex.org/W1967969088","https://openalex.org/W1973948212","https://openalex.org/W2009499611","https://openalex.org/W2037733739","https://openalex.org/W2042084565","https://openalex.org/W2047672715","https://openalex.org/W2052629792","https://openalex.org/W2065540158","https://openalex.org/W2072250033","https://openalex.org/W2076443308","https://openalex.org/W2081724349","https://openalex.org/W2089013648","https://openalex.org/W2094592342","https://openalex.org/W2103041471","https://openalex.org/W2109109045","https://openalex.org/W2117812871","https://openalex.org/W2129476886","https://openalex.org/W2130177815","https://openalex.org/W2151499551","https://openalex.org/W2152280271","https://openalex.org/W2156131304","https://openalex.org/W2168175751","https://openalex.org/W2171277043","https://openalex.org/W2238130397","https://openalex.org/W2331130507","https://openalex.org/W2490800645","https://openalex.org/W2797583072","https://openalex.org/W2954364924","https://openalex.org/W3014310718","https://openalex.org/W4235611253","https://openalex.org/W4237222446","https://openalex.org/W4249303080","https://openalex.org/W4295134715"],"related_works":["https://openalex.org/W2350751952","https://openalex.org/W2362114017","https://openalex.org/W1999647744","https://openalex.org/W3147024994","https://openalex.org/W1978302214","https://openalex.org/W2374055396","https://openalex.org/W2063246903","https://openalex.org/W2021817983","https://openalex.org/W3021047493","https://openalex.org/W3008559849"],"abstract_inverted_index":{"We":[0],"consider":[1],"the":[2,29,55,58,79],"problem":[3],"of":[4,57,81,86],"constructing":[5],"multi-class":[6,60],"classification":[7],"methods":[8],"for":[9],"analyzing":[10],"data":[11],"with":[12],"complex":[13],"structure.":[14],"A":[15],"nonlinear":[16],"logistic":[17],"discriminant":[18,61],"model":[19,40],"is":[20,66],"introduced":[21],"based":[22],"on":[23],"Gaussian":[24],"basis":[25],"functions":[26],"constructed":[27],"by":[28],"self-organizing":[30],"map.":[31],"In":[32],"order":[33],"to":[34,53,69],"select":[35],"adjusted":[36],"parameters,":[37],"we":[38],"employ":[39],"selection":[41],"criteria":[42],"derived":[43],"from":[44],"information-theoretic":[45],"and":[46],"Bayesian":[47],"approaches.":[48],"Numerical":[49],"examples":[50],"are":[51],"conducted":[52],"investigate":[54],"performance":[56],"proposed":[59],"procedure.":[62],"Our":[63],"modeling":[64],"procedure":[65],"also":[67],"applied":[68],"protein":[70],"structure":[71],"recognition":[72],"in":[73,84],"life":[74],"science.":[75],"The":[76],"results":[77],"indicate":[78],"effectiveness":[80],"our":[82],"strategy":[83],"terms":[85],"prediction":[87],"accuracy.":[88]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2016,"cited_by_count":1},{"year":2015,"cited_by_count":2},{"year":2014,"cited_by_count":1},{"year":2013,"cited_by_count":2},{"year":2012,"cited_by_count":3}],"updated_date":"2026-04-05T17:49:38.594831","created_date":"2025-10-10T00:00:00"}