Towards learning in parallel universes

Lade...
Vorschaubild
Dateien
Towards_learning_in_parallel_universes.pdf
Towards_learning_in_parallel_universes.pdfGröße: 867.24 KBDownloads: 334
Datum
2004
Autor:innen
Patterson, David E.
Herausgeber:innen
Kontakt
ISSN der Zeitschrift
Electronic ISSN
ISBN
Bibliografische Daten
Verlag
Schriftenreihe
Auflagebezeichnung
ArXiv-ID
Internationale Patentnummer
Angaben zur Forschungsförderung
Projekt
Open Access-Veröffentlichung
Open Access Green
Core Facility der Universität Konstanz
Gesperrt bis
Titel in einer weiteren Sprache
Forschungsvorhaben
Organisationseinheiten
Zeitschriftenheft
Publikationstyp
Beitrag zu einem Konferenzband
Publikationsstatus
Published
Erschienen in
2004 IEEE International Conference on Fuzzy Systems (IEEE Cat. No.04CH37542). IEEE, 2004, pp. 67-71. ISBN 0-7803-8353-2. Available under: doi: 10.1109/FUZZY.2004.1375689
Zusammenfassung

Most learning algorithms operate in a clearly defined feature space and assume that all relevant structure can he found in this one, single space. For many local learning methods, especially the ones working on distance metrics (e.g. clustering algorithms), this poses a serious limitation. We disucss an algorithm that directly finds a set of cluster centers based on an analysis of the distribution of patterns in the local neighborhood of each potential cluster center through the use of so-called Neighborgrams. This type of cluster construction makes it feasable to find clusters in several feature spaces in parallel, effectively finding the optimal feature space for each cluster independently. We demonstrate how the algorithm works on an artificial data set and show its usefulness using a well-known benchmark data set.

Zusammenfassung in einer weiteren Sprache
Fachgebiet (DDC)
004 Informatik
Schlagwörter
Konferenz
2004 IEEE International Conference on Fuzzy Systems, Budapest, Hungary
Rezension
undefined / . - undefined, undefined
Zitieren
ISO 690BERTHOLD, Michael R., David E. PATTERSON, 2004. Towards learning in parallel universes. 2004 IEEE International Conference on Fuzzy Systems. Budapest, Hungary. In: 2004 IEEE International Conference on Fuzzy Systems (IEEE Cat. No.04CH37542). IEEE, 2004, pp. 67-71. ISBN 0-7803-8353-2. Available under: doi: 10.1109/FUZZY.2004.1375689
BibTex
@inproceedings{Berthold2004Towar-5466,
  year={2004},
  doi={10.1109/FUZZY.2004.1375689},
  title={Towards learning in parallel universes},
  isbn={0-7803-8353-2},
  publisher={IEEE},
  booktitle={2004 IEEE International Conference on Fuzzy Systems (IEEE Cat. No.04CH37542)},
  pages={67--71},
  author={Berthold, Michael R. and Patterson, David E.}
}
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/123456789/5466">
    <dc:contributor>Berthold, Michael R.</dc:contributor>
    <dcterms:issued>2004</dcterms:issued>
    <dc:format>application/pdf</dc:format>
    <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2011-03-24T15:55:38Z</dcterms:available>
    <bibo:uri rdf:resource="http://kops.uni-konstanz.de/handle/123456789/5466"/>
    <void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/>
    <dc:creator>Berthold, Michael R.</dc:creator>
    <dcterms:title>Towards learning in parallel universes</dcterms:title>
    <foaf:homepage rdf:resource="http://localhost:8080/"/>
    <dcterms:rights rdf:resource="http://creativecommons.org/licenses/by-nc-nd/2.0/"/>
    <dcterms:abstract xml:lang="eng">Most learning algorithms operate in a clearly defined feature space and assume that all relevant structure can he found in this one, single space. For many local learning methods, especially the ones working on distance metrics (e.g. clustering algorithms), this poses a serious limitation. We disucss an algorithm that directly finds a set of cluster centers based on an analysis of the distribution of patterns in the local neighborhood of each potential cluster center through the use of so-called Neighborgrams. This type of cluster construction makes it feasable to find clusters in several feature spaces in parallel, effectively finding the optimal feature space for each cluster independently. We demonstrate how the algorithm works on an artificial data set and show its usefulness using a well-known benchmark data set.</dcterms:abstract>
    <dcterms:isPartOf rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/>
    <dc:contributor>Patterson, David E.</dc:contributor>
    <dcterms:bibliographicCitation>First publ. in: Proceedings / 2004 IEEE International Conference on Fuzzy Systems : Budapest, Hungary, 25 - 29 July 2004, pp. 67-71</dcterms:bibliographicCitation>
    <dspace:hasBitstream rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/5466/1/Towards_learning_in_parallel_universes.pdf"/>
    <dc:language>eng</dc:language>
    <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2011-03-24T15:55:38Z</dc:date>
    <dc:creator>Patterson, David E.</dc:creator>
    <dc:rights>Attribution-NonCommercial-NoDerivs 2.0 Generic</dc:rights>
    <dspace:isPartOfCollection rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/>
    <dcterms:hasPart rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/5466/1/Towards_learning_in_parallel_universes.pdf"/>
  </rdf:Description>
</rdf:RDF>
Interner Vermerk
xmlui.Submission.submit.DescribeStep.inputForms.label.kops_note_fromSubmitter
Kontakt
URL der Originalveröffentl.
Prüfdatum der URL
Prüfungsdatum der Dissertation
Finanzierungsart
Kommentar zur Publikation
Allianzlizenz
Corresponding Authors der Uni Konstanz vorhanden
Internationale Co-Autor:innen
Universitätsbibliographie
Ja
Begutachtet
Diese Publikation teilen