@prefix ocrer: <http://purl.org/net/OCRe/research.owl#> .
@prefix owl:   <http://www.w3.org/2002/07/owl#> .
@prefix scires: <http://vivoweb.org/ontology/scientific-research#> .
@prefix xsd:   <http://www.w3.org/2001/XMLSchema#> .
@prefix skos:  <http://www.w3.org/2004/02/skos/core#> .
@prefix rdfs:  <http://www.w3.org/2000/01/rdf-schema#> .
@prefix ocresd: <http://purl.org/net/OCRe/study_design.owl#> .
@prefix swo:   <http://www.ebi.ac.uk/efo/swo/> .
@prefix cito:  <http://purl.org/spar/cito/> .
@prefix geo:   <http://aims.fao.org/aos/geopolitical.owl#> .
@prefix ocresst: <http://purl.org/net/OCRe/statistics.owl#> .
@prefix dcterms: <http://purl.org/dc/terms/> .
@prefix vivo:  <http://vivoweb.org/ontology/core#> .
@prefix event: <http://purl.org/NET/c4dm/event.owl#> .
@prefix vann:  <http://purl.org/vocab/vann/> .
@prefix foaf:  <http://xmlns.com/foaf/0.1/> .
@prefix c4o:   <http://purl.org/spar/c4o/> .
@prefix fabio: <http://purl.org/spar/fabio/> .
@prefix vcard: <http://www.w3.org/2006/vcard/ns#> .
@prefix thkoeln: <http://cris.nrw/hisinone#> .
@prefix vitro: <http://vitro.mannlib.cornell.edu/ns/vitro/0.7#> .
@prefix vitro-public: <http://vitro.mannlib.cornell.edu/ns/vitro/public#> .
@prefix rdf:   <http://www.w3.org/1999/02/22-rdf-syntax-ns#> .
@prefix ocresp: <http://purl.org/net/OCRe/study_protocol.owl#> .
@prefix bibo:  <http://purl.org/ontology/bibo/> .
@prefix obo:   <http://purl.obolibrary.org/obo/> .
@prefix ro:    <http://purl.obolibrary.org/obo/ro.owl#> .

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        rdfs:label  "Institut für Data Science, Engineering, and Analytics" ;
        <http://cris.nrw/hisinone/istOrganisationseinheitVon>
                <https://fis.th-koeln.de/vivo/individual/publ_12992> .

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        a                         foaf:Document , obo:BFO_0000002 , obo:BFO_0000031 , owl:Thing , thkoeln:Publikation , obo:BFO_0000001 , thkoeln:Journalartikel , obo:IAO_0000030 , thkoeln:Artikel , bibo:Document ;
        rdfs:label                "RDF description of Optimally Weighted Ensembles of Regression Models - https://fis.th-koeln.de/vivo/individual/publ_12992" , "Optimally Weighted Ensembles of Regression Models" ;
        thkoeln:dokumententyp     "Wissenschaftlicher Artikel" ;
        thkoeln:sprache           "englisch" ;
        thkoeln:status            "validiert" ;
        thkoeln:subtitle          "Exact Weight Optimization and Applications" ;
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                <https://fis.th-koeln.de/vivo/individual/FF_001> ;
        <http://cris.nrw/hisinone/hatOrganisationseinheit>
                <https://fis.th-koeln.de/vivo/individual/11000300> , <https://fis.th-koeln.de/vivo/individual/10000126> , <https://fis.th-koeln.de/vivo/individual/eigene_HS> ;
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                <https://fis.th-koeln.de/vivo/individual/cite_12992-2> , <https://fis.th-koeln.de/vivo/individual/cite_12992-1> , <https://fis.th-koeln.de/vivo/individual/cite_12992-0> ;
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        <http://purl.org/dc/elements/1.1/date>
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        bibo:abstract             "Automated model selection is often proposed to users to choose which machine learning model (or method) to apply to a given regression task. In this paper, we show that combining different regression models can yield better results than selecting a single ('best') regression model, and outline an efficient method that obtains optimally weighted convex linear combination from a heterogeneous set of regression models. More specifically, in this paper, a heuristic weight optimization, used in a preceding conference paper, is replaced by an exact optimization algorithm using convex quadratic programming. We prove convexity of the quadratic programming formulation for the straightforward formulation and for a formulation with weighted data points. The novel weight optimization is not only (more) exact but also more efficient. The methods we develop in this paper are implemented and made available via github-open source. They can be executed on commonly available hardware and offer a transparent and easy to interpret interface. The results indicate that the approach outperforms model selection methods on a range of data sets, including data sets with mixed variable type from drug discovery applications." ;
        bibo:doi                  "10.48550/arXiv.2206.11263" ;
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        vivo:dateTimeValue        <http://cris.nrw/date2022> ;
        vivo:freetextKeyword      "Machine Learning (cs.LG)" , "62J02" , "I.2" , "FOS: Computer and Information Sciences" , "I.2.6" ;
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        a       thkoeln:Zitat , owl:Thing .

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        rdfs:label                "De.arXiv.org" ;
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        rdfs:label  "Publikationen"@de-DE , "Publications"@en-US .

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        rdfs:label  "Institut für Informationswissenschaft" ;
        <http://cris.nrw/hisinone/istOrganisationseinheitVon>
                <https://fis.th-koeln.de/vivo/individual/publ_12992> .

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        a             obo:BFO_0000001 , obo:BFO_0000020 , vivo:Authorship , vivo:Relationship , obo:BFO_0000002 , owl:Thing ;
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