Supervised Learning in Parallel Universes using Neighborgrams

Kurzanzeige
dc.contributor.authorWiswedel, Bernd
dc.contributor.authorBerthold, Michael R.
dc.date.accessioned2012-05-22T09:11:57Zdeu
dc.date.available2012-05-22T09:11:57Zdeu
dc.date.issued2011
dc.description.abstractWe present a supervised method for Learning in Parallel Universes, i.e. problems given in multiple descriptor spaces. The goal is the construction of local models in individual universes and their fusion to a superior global model that comprises all the available information from the given universes. We employ a predictive clustering approach using Neighborgrams, a one-dimensional data structure for the neighborhood of a single object in a universe. We also present an intuitive visualization, which allows for interactive model construction and visual comparison of cluster neighborhoods across universes.eng
dc.description.versionpublished
dc.identifier.citationAdvances in intelligent data analysis X : 10th international symposium, IDA 2011, Porto, Portugal, October 29 - 31, 2011 ; proceedings / João Gama... (eds.). - Heidelberg [u.a.] : Springer, 2011. - S. 388-400. - (Lecture notes in computer science ; 7014). - ISBN 978-3-642-24799-6deu
dc.identifier.doi10.1007/978-3-642-24800-9_36deu
dc.identifier.ppn383926831deu
dc.identifier.urihttp://kops.uni-konstanz.de/handle/123456789/19324
dc.language.isoengdeu
dc.legacy.dateIssued2012-05-22deu
dc.rightsterms-of-usedeu
dc.rights.urihttps://rightsstatements.org/page/InC/1.0/deu
dc.subject.ddc004deu
dc.titleSupervised Learning in Parallel Universes using Neighborgramseng
dc.typeINPROCEEDINGSdeu
dspace.entity.typePublication
kops.citation.bibtex
@inproceedings{Wiswedel2011Super-19324,
  year={2011},
  doi={10.1007/978-3-642-24800-9_36},
  title={Supervised Learning in Parallel Universes using Neighborgrams},
  number={7014},
  isbn={978-3-642-24799-6},
  publisher={Springer Berlin Heidelberg},
  address={Berlin, Heidelberg},
  series={Lecture Notes in Computer Science},
  booktitle={Advances in Intelligent Data Analysis X},
  pages={388--400},
  editor={Gama, João and Bradley, Elizabeth and Hollmén, Jaakko},
  author={Wiswedel, Bernd and Berthold, Michael R.}
}
kops.citation.iso690WISWEDEL, Bernd, Michael R. BERTHOLD, 2011. Supervised Learning in Parallel Universes using Neighborgrams. In: GAMA, João, ed., Elizabeth BRADLEY, ed., Jaakko HOLLMÉN, ed.. Advances in Intelligent Data Analysis X. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011, pp. 388-400. Lecture Notes in Computer Science. 7014. ISBN 978-3-642-24799-6. Available under: doi: 10.1007/978-3-642-24800-9_36deu
kops.citation.iso690WISWEDEL, Bernd, Michael R. BERTHOLD, 2011. Supervised Learning in Parallel Universes using Neighborgrams. In: GAMA, João, ed., Elizabeth BRADLEY, ed., Jaakko HOLLMÉN, ed.. Advances in Intelligent Data Analysis X. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011, pp. 388-400. Lecture Notes in Computer Science. 7014. ISBN 978-3-642-24799-6. Available under: doi: 10.1007/978-3-642-24800-9_36eng
kops.citation.rdf
<rdf:RDF
    xmlns:dcterms="http://purl.org/dc/terms/"
    xmlns:dc="http://purl.org/dc/elements/1.1/"
    xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"
    xmlns:bibo="http://purl.org/ontology/bibo/"
    xmlns:dspace="http://digital-repositories.org/ontologies/dspace/0.1.0#"
    xmlns:foaf="http://xmlns.com/foaf/0.1/"
    xmlns:void="http://rdfs.org/ns/void#"
    xmlns:xsd="http://www.w3.org/2001/XMLSchema#" > 
  <rdf:Description rdf:about="https://kops.uni-konstanz.de/server/rdf/resource/123456789/19324">
    <dc:contributor>Berthold, Michael R.</dc:contributor>
    <dc:rights>terms-of-use</dc:rights>
    <dcterms:abstract xml:lang="eng">We present a supervised method for Learning in Parallel Universes, i.e. problems given in multiple descriptor spaces. The goal is the construction of local models in individual universes and their fusion to a superior global model that comprises all the available information from the given universes. We employ a predictive clustering approach using Neighborgrams, a one-dimensional data structure for the neighborhood of a single object in a universe. We also present an intuitive visualization, which allows for interactive model construction and visual comparison of cluster neighborhoods across universes.</dcterms:abstract>
    <dcterms:bibliographicCitation>Advances in intelligent data analysis X : 10th international symposium, IDA 2011, Porto, Portugal, October 29 - 31, 2011 ; proceedings / João Gama... (eds.). - Heidelberg [u.a.] : Springer, 2011. - S. 388-400. - (Lecture notes in computer science ; 7014). - ISBN 978-3-642-24799-6</dcterms:bibliographicCitation>
    <void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/>
    <dc:creator>Wiswedel, Bernd</dc:creator>
    <dcterms:hasPart rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/19324/2/Supervised%20Learning-erl.pdf"/>
    <dc:language>eng</dc:language>
    <dc:contributor>Wiswedel, Bernd</dc:contributor>
    <dcterms:issued>2011</dcterms:issued>
    <dcterms:title>Supervised Learning in Parallel Universes using Neighborgrams</dcterms:title>
    <dspace:isPartOfCollection rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/>
    <dspace:hasBitstream rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/19324/2/Supervised%20Learning-erl.pdf"/>
    <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2012-05-22T09:11:57Z</dcterms:available>
    <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2012-05-22T09:11:57Z</dc:date>
    <foaf:homepage rdf:resource="http://localhost:8080/"/>
    <bibo:uri rdf:resource="http://kops.uni-konstanz.de/handle/123456789/19324"/>
    <dcterms:rights rdf:resource="https://rightsstatements.org/page/InC/1.0/"/>
    <dc:creator>Berthold, Michael R.</dc:creator>
    <dcterms:isPartOf rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/>
  </rdf:Description>
</rdf:RDF>
kops.description.openAccessopenaccessgreen
kops.flag.knbibliographytrue
kops.identifier.nbnurn:nbn:de:bsz:352-193242deu
kops.sourcefieldGAMA, João, ed., Elizabeth BRADLEY, ed., Jaakko HOLLMÉN, ed.. <i>Advances in Intelligent Data Analysis X</i>. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011, pp. 388-400. Lecture Notes in Computer Science. 7014. ISBN 978-3-642-24799-6. Available under: doi: 10.1007/978-3-642-24800-9_36deu
kops.sourcefield.plainGAMA, João, ed., Elizabeth BRADLEY, ed., Jaakko HOLLMÉN, ed.. Advances in Intelligent Data Analysis X. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011, pp. 388-400. Lecture Notes in Computer Science. 7014. ISBN 978-3-642-24799-6. Available under: doi: 10.1007/978-3-642-24800-9_36deu
kops.sourcefield.plainGAMA, João, ed., Elizabeth BRADLEY, ed., Jaakko HOLLMÉN, ed.. Advances in Intelligent Data Analysis X. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011, pp. 388-400. Lecture Notes in Computer Science. 7014. ISBN 978-3-642-24799-6. Available under: doi: 10.1007/978-3-642-24800-9_36eng
kops.submitter.emaillarysa.herasymova@uni-konstanz.dedeu
relation.isAuthorOfPublication2a49b46f-c021-4e84-ade3-969e85f2dbb6
relation.isAuthorOfPublication56ea9ab6-14a4-493e-8ef1-3c064e0c50a1
relation.isAuthorOfPublication.latestForDiscovery2a49b46f-c021-4e84-ade3-969e85f2dbb6
source.bibliographicInfo.fromPage388
source.bibliographicInfo.seriesNumber7014
source.bibliographicInfo.toPage400
source.contributor.editorGama, João
source.contributor.editorBradley, Elizabeth
source.contributor.editorHollmén, Jaakko
source.identifier.isbn978-3-642-24799-6
source.publisherSpringer Berlin Heidelberg
source.publisher.locationBerlin, Heidelberg
source.relation.ispartofseriesLecture Notes in Computer Science
source.titleAdvances in Intelligent Data Analysis X

Dateien

Originalbündel

Gerade angezeigt 1 - 1 von 1
Vorschaubild nicht verfügbar
Name:
Supervised Learning-erl.pdf
Größe:
5.98 MB
Format:
Adobe Portable Document Format
Supervised Learning-erl.pdf
Supervised Learning-erl.pdfGröße: 5.98 MBDownloads: 567

Lizenzbündel

Gerade angezeigt 1 - 1 von 1
Vorschaubild nicht verfügbar
Name:
license.txt
Größe:
1.92 KB
Format:
Plain Text
Beschreibung:
license.txt
license.txtGröße: 1.92 KBDownloads: 0

Sammlungen

Informatik und Informationswissenschaft: Publikationen