{"id":"https://openalex.org/W2959815030","doi":"https://doi.org/10.1007/s00521-019-04299-2","title":"Adaptive basis functions for prototype-based classification of functional data","display_name":"Adaptive basis functions for prototype-based classification of functional data","publication_year":2019,"publication_date":"2019-07-13","ids":{"openalex":"https://openalex.org/W2959815030","doi":"https://doi.org/10.1007/s00521-019-04299-2","mag":"2959815030"},"language":"en","primary_location":{"id":"doi:10.1007/s00521-019-04299-2","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s00521-019-04299-2","pdf_url":"https://link.springer.com/content/pdf/10.1007/s00521-019-04299-2.pdf","source":{"id":"https://openalex.org/S147897268","display_name":"Neural Computing and Applications","issn_l":"0941-0643","issn":["0941-0643","1433-3058"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Neural Computing and Applications","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://link.springer.com/content/pdf/10.1007/s00521-019-04299-2.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5079264070","display_name":"Friedrich Melchert","orcid":"https://orcid.org/0000-0002-9145-2277"},"institutions":[{"id":"https://openalex.org/I169381384","display_name":"University of Groningen","ror":"https://ror.org/012p63287","country_code":"NL","type":"education","lineage":["https://openalex.org/I169381384"]},{"id":"https://openalex.org/I4210139350","display_name":"Fraunhofer Institute for Factory Operation and Automation","ror":"https://ror.org/04qfaak15","country_code":"DE","type":"facility","lineage":["https://openalex.org/I4210139350","https://openalex.org/I4923324"]}],"countries":["DE","NL"],"is_corresponding":false,"raw_author_name":"Friedrich Melchert","raw_affiliation_strings":["Fraunhofer Institute for Factory Operation and Automation IFF, Magdeburg, Germany","Johann Bernoulli Institute for Mathematics and Computer Science, University of Groningen, Groningen, The Netherlands"],"raw_orcid":"https://orcid.org/0000-0002-9145-2277","affiliations":[{"raw_affiliation_string":"Fraunhofer Institute for Factory Operation and Automation IFF, Magdeburg, Germany","institution_ids":["https://openalex.org/I4210139350"]},{"raw_affiliation_string":"Johann Bernoulli Institute for Mathematics and Computer Science, University of Groningen, Groningen, The Netherlands","institution_ids":["https://openalex.org/I169381384"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5018753288","display_name":"Gabriele Bani","orcid":null},"institutions":[{"id":"https://openalex.org/I887064364","display_name":"University of Amsterdam","ror":"https://ror.org/04dkp9463","country_code":"NL","type":"education","lineage":["https://openalex.org/I887064364"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Gabriele Bani","raw_affiliation_strings":["Faculty of Science, University of Amsterdam, Amsterdam, The Netherlands"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Faculty of Science, University of Amsterdam, Amsterdam, The Netherlands","institution_ids":["https://openalex.org/I887064364"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5035286804","display_name":"Udo Seiffert","orcid":"https://orcid.org/0000-0002-6043-7947"},"institutions":[{"id":"https://openalex.org/I4210139350","display_name":"Fraunhofer Institute for Factory Operation and Automation","ror":"https://ror.org/04qfaak15","country_code":"DE","type":"facility","lineage":["https://openalex.org/I4210139350","https://openalex.org/I4923324"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Udo Seiffert","raw_affiliation_strings":["Fraunhofer Institute for Factory Operation and Automation IFF, Magdeburg, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Fraunhofer Institute for Factory Operation and Automation IFF, Magdeburg, Germany","institution_ids":["https://openalex.org/I4210139350"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5083548477","display_name":"Michael Biehl","orcid":"https://orcid.org/0000-0001-5148-4568"},"institutions":[{"id":"https://openalex.org/I169381384","display_name":"University of Groningen","ror":"https://ror.org/012p63287","country_code":"NL","type":"education","lineage":["https://openalex.org/I169381384"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Michael Biehl","raw_affiliation_strings":["Johann Bernoulli Institute for Mathematics and Computer Science, University of Groningen, Groningen, The Netherlands"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Johann Bernoulli Institute for Mathematics and Computer Science, University of Groningen, Groningen, The Netherlands","institution_ids":["https://openalex.org/I169381384"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":2390,"currency":"EUR","value_usd":2990},"apc_paid":{"value":2390,"currency":"EUR","value_usd":2990},"fwci":0.8676,"has_fulltext":true,"cited_by_count":7,"citation_normalized_percentile":{"value":0.80819327,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":97},"biblio":{"volume":"32","issue":"24","first_page":"18213","last_page":"18223"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.9941999912261963,"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":0.9941999912261963,"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/T10908","display_name":"Analytical Chemistry and Chromatography","score":0.983299970626831,"subfield":{"id":"https://openalex.org/subfields/1607","display_name":"Spectroscopy"},"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/T11236","display_name":"Control Systems and Identification","score":0.9812999963760376,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.684550940990448},{"id":"https://openalex.org/keywords/basis","display_name":"Basis (linear algebra)","score":0.6467465162277222},{"id":"https://openalex.org/keywords/basis-function","display_name":"Basis function","score":0.6318035125732422},{"id":"https://openalex.org/keywords/functional-principal-component-analysis","display_name":"Functional principal component analysis","score":0.622211217880249},{"id":"https://openalex.org/keywords/dimensionality-reduction","display_name":"Dimensionality reduction","score":0.6114116311073303},{"id":"https://openalex.org/keywords/functional-data-analysis","display_name":"Functional data analysis","score":0.5226640105247498},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5145472884178162},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4879869818687439},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4538915157318115},{"id":"https://openalex.org/keywords/raw-data","display_name":"Raw data","score":0.4376369118690491},{"id":"https://openalex.org/keywords/principal-component-analysis","display_name":"Principal component analysis","score":0.4277825653553009},{"id":"https://openalex.org/keywords/data-set","display_name":"Data set","score":0.4159560799598694},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3492145240306854},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3420431613922119},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.26362401247024536}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.684550940990448},{"id":"https://openalex.org/C12426560","wikidata":"https://www.wikidata.org/wiki/Q189569","display_name":"Basis (linear algebra)","level":2,"score":0.6467465162277222},{"id":"https://openalex.org/C5917680","wikidata":"https://www.wikidata.org/wiki/Q2621825","display_name":"Basis function","level":2,"score":0.6318035125732422},{"id":"https://openalex.org/C71176878","wikidata":"https://www.wikidata.org/wiki/Q17014987","display_name":"Functional principal component analysis","level":3,"score":0.622211217880249},{"id":"https://openalex.org/C70518039","wikidata":"https://www.wikidata.org/wiki/Q16000077","display_name":"Dimensionality reduction","level":2,"score":0.6114116311073303},{"id":"https://openalex.org/C51820054","wikidata":"https://www.wikidata.org/wiki/Q5508814","display_name":"Functional data analysis","level":2,"score":0.5226640105247498},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5145472884178162},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4879869818687439},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4538915157318115},{"id":"https://openalex.org/C132964779","wikidata":"https://www.wikidata.org/wiki/Q2110223","display_name":"Raw data","level":2,"score":0.4376369118690491},{"id":"https://openalex.org/C27438332","wikidata":"https://www.wikidata.org/wiki/Q2873","display_name":"Principal component analysis","level":2,"score":0.4277825653553009},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.4159560799598694},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3492145240306854},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3420431613922119},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.26362401247024536},{"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/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","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}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.1007/s00521-019-04299-2","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s00521-019-04299-2","pdf_url":"https://link.springer.com/content/pdf/10.1007/s00521-019-04299-2.pdf","source":{"id":"https://openalex.org/S147897268","display_name":"Neural Computing and Applications","issn_l":"0941-0643","issn":["0941-0643","1433-3058"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Neural Computing and Applications","raw_type":"journal-article"},{"id":"pmh:oai:pure.rug.nl:publications/eb2a2fae-48ae-4050-b0b8-84aad6fd5226","is_oa":true,"landing_page_url":"https://research.rug.nl/en/publications/eb2a2fae-48ae-4050-b0b8-84aad6fd5226","pdf_url":"https://pure.rug.nl/ws/files/158957830/Melchert2020_Article_AdaptiveBasisFunctionsForProto.pdf","source":{"id":"https://openalex.org/S4306400420","display_name":"University of Groningen research database (University of Groningen / Centre for Information Technology)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I169381384","host_organization_name":"University of Groningen","host_organization_lineage":["https://openalex.org/I169381384"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Melchert, F, Bani, G, Seiffert, U & Biehl, M 2020, 'Adaptive basis functions for prototype-based classification of functional data', Neural Computing and Applications, vol. 32, no. 24, pp. 18213-18223. https://doi.org/10.1007/s00521-019-04299-2","raw_type":"info:eu-repo/semantics/publishedVersion"},{"id":"pmh:oai:fraunhofer.de:N-558883","is_oa":true,"landing_page_url":"http://publica.fraunhofer.de/documents/N-558883.html","pdf_url":null,"source":{"id":"https://openalex.org/S4306400801","display_name":"Publikationsdatenbank der Fraunhofer-Gesellschaft (Fraunhofer-Gesellschaft)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4923324","host_organization_name":"Fraunhofer-Gesellschaft","host_organization_lineage":["https://openalex.org/I4923324"],"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":"Fraunhofer IFF","raw_type":"Journal Article"},{"id":"pmh:oai:publica.fraunhofer.de:publica/258761","is_oa":false,"landing_page_url":"https://publica.fraunhofer.de/handle/publica/258761","pdf_url":null,"source":{"id":"https://openalex.org/S4306400318","display_name":"Fraunhofer-Publica (Fraunhofer-Gesellschaft)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4923324","host_organization_name":"Fraunhofer-Gesellschaft","host_organization_lineage":["https://openalex.org/I4923324"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"journal article"}],"best_oa_location":{"id":"doi:10.1007/s00521-019-04299-2","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s00521-019-04299-2","pdf_url":"https://link.springer.com/content/pdf/10.1007/s00521-019-04299-2.pdf","source":{"id":"https://openalex.org/S147897268","display_name":"Neural Computing and Applications","issn_l":"0941-0643","issn":["0941-0643","1433-3058"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Neural Computing and Applications","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2959815030.pdf","grobid_xml":"https://content.openalex.org/works/W2959815030.grobid-xml"},"referenced_works_count":36,"referenced_works":["https://openalex.org/W25311418","https://openalex.org/W1576043040","https://openalex.org/W1594031697","https://openalex.org/W1676820704","https://openalex.org/W1679913846","https://openalex.org/W1967320885","https://openalex.org/W2036245684","https://openalex.org/W2039434802","https://openalex.org/W2093827448","https://openalex.org/W2094150678","https://openalex.org/W2097296149","https://openalex.org/W2101927907","https://openalex.org/W2103595817","https://openalex.org/W2111823723","https://openalex.org/W2119821739","https://openalex.org/W2123749980","https://openalex.org/W2126120145","https://openalex.org/W2134155547","https://openalex.org/W2143060555","https://openalex.org/W2146245281","https://openalex.org/W2274386229","https://openalex.org/W2558649365","https://openalex.org/W2580082328","https://openalex.org/W2581918926","https://openalex.org/W2730878775","https://openalex.org/W2738563279","https://openalex.org/W2751430773","https://openalex.org/W2794869810","https://openalex.org/W2964121744","https://openalex.org/W3085162807","https://openalex.org/W4212774754","https://openalex.org/W4213332169","https://openalex.org/W4234535302","https://openalex.org/W4239510810","https://openalex.org/W4256561644","https://openalex.org/W6726375174"],"related_works":["https://openalex.org/W1984827805","https://openalex.org/W1965253260","https://openalex.org/W2771038650","https://openalex.org/W4381795696","https://openalex.org/W2031910971","https://openalex.org/W4210655154","https://openalex.org/W91700548","https://openalex.org/W4323546569","https://openalex.org/W2611307339","https://openalex.org/W2006947027"],"abstract_inverted_index":{"Abstract":[0],"We":[1,11],"present":[2],"a":[3,42,54,63,92,110,150],"framework":[4],"for":[5,60,149],"distance-based":[6],"classification":[7,85,125],"of":[8,15,24,38,45,52,57,67,89,131,135,153],"functional":[9,40,43,70,80,113],"data.":[10],"consider":[12],"the":[13,39,46,61,78,84,90,99,119,129,132,142],"analysis":[14,130],"labeled":[16],"spectral":[17],"data":[18,48,107,143],"and":[19,145],"time":[20],"series":[21],"by":[22,109],"means":[23],"generalized":[25],"matrix":[26],"relevance":[27],"learning":[28],"vector":[29],"quantization":[30],"(GMLVQ)":[31],"as":[32,103,105],"an":[33,68,147],"example.":[34],"To":[35],"take":[36],"advantage":[37],"nature,":[41],"expansion":[44],"input":[47,101],"is":[49,72,75,95],"considered.":[50],"Instead":[51],"using":[53],"predefined":[55,112],"set":[56,134],"basis":[58,71,136],"functions":[59,137],"expansion,":[62],"more":[64],"flexible":[65],"scheme":[66],"adaptive":[69],"employed.":[73],"GMLVQ":[74,93],"applied":[76,97],"on":[77,106],"resulting":[79],"parameters":[81],"to":[82,98,123],"solve":[83],"task.":[86],"For":[87],"comparison":[88],"classification,":[91],"system":[94],"also":[96],"raw":[100],"data,":[102],"well":[104],"expanded":[108],"different":[111],"basis.":[114],"Computer":[115],"experiments":[116],"show":[117],"that":[118],"methods":[120],"offer":[121],"potential":[122],"improve":[124],"performance":[126],"significantly.":[127],"Furthermore,":[128],"adapted":[133],"give":[138],"further":[139],"insights":[140],"into":[141],"structure":[144],"yields":[146],"option":[148],"drastic":[151],"reduction":[152],"dimensionality.":[154]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":3}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
