Publikation:

HARAM : a Hierarchical ARAM Neural Network for Large-Scale Text Classification

Lade...
Vorschaubild

Dateien

Zu diesem Dokument gibt es keine Dateien.

Datum

2015

Herausgeber:innen

Kontakt

ISSN der Zeitschrift

Electronic ISSN

ISBN

Bibliografische Daten

Verlag

Schriftenreihe

Auflagebezeichnung

URI (zitierfähiger Link)
ArXiv-ID

Internationale Patentnummer

Angaben zur Forschungsförderung

Projekt

Open Access-Veröffentlichung
Core Facility der Universität Konstanz

Gesperrt bis

Titel in einer weiteren Sprache

Publikationstyp
Beitrag zu einem Konferenzband
Publikationsstatus
Published

Erschienen in

CUI, Peng, ed. and others. 15th IEEE International Conference on Data Mining Workshop : Proceedings ; 14–17 November 2015, Atlantic City, New Jersey. Los Alamitos, CA: IEEE, 2015, pp. 847-854. ISBN 978-1-4673-8493-3. Available under: doi: 10.1109/ICDMW.2015.14

Zusammenfassung

With the rapid development of the Web, the need for text classification of large data volumes is permanently growing. Texts represented as bags-of-words possess usually very high dimensionality in the input space and often also in the output space if labeled with many categories. As a result, neural classifiers should be adapted to such large-scale data. We present here a well scalable extension to the fuzzy Adaptive Resonance Associative Map (ARAM) neural network which was specially developed for high-dimensional and large data. This extension aims at increasing the classification speed by adding an extra ART layer for clustering learned prototypes into large clusters. In this case the activation of all prototypes can be replaced by the activation of a small fraction of them, leading to a significant reduction of the classification time. This extension can be especially useful for multi-label classification tasks.

Zusammenfassung in einer weiteren Sprache

Fachgebiet (DDC)
004 Informatik

Schlagwörter

Konferenz

15th IEEE International Conference on Data Mining Workshop (ICDMW 2015), 14. Nov. 2015 - 17. Nov. 2015, Atlantic City, NJ, USA
Rezension
undefined / . - undefined, undefined

Forschungsvorhaben

Organisationseinheiten

Zeitschriftenheft

Zugehörige Datensätze in KOPS

Zitieren

ISO 690BENITES, Fernando, Elena SAPOZHNIKOVA, 2015. HARAM : a Hierarchical ARAM Neural Network for Large-Scale Text Classification. 15th IEEE International Conference on Data Mining Workshop (ICDMW 2015). Atlantic City, NJ, USA, 14. Nov. 2015 - 17. Nov. 2015. In: CUI, Peng, ed. and others. 15th IEEE International Conference on Data Mining Workshop : Proceedings ; 14–17 November 2015, Atlantic City, New Jersey. Los Alamitos, CA: IEEE, 2015, pp. 847-854. ISBN 978-1-4673-8493-3. Available under: doi: 10.1109/ICDMW.2015.14
BibTex
@inproceedings{Benites2015-11HARAM-33471,
  year={2015},
  doi={10.1109/ICDMW.2015.14},
  title={HARAM : a Hierarchical ARAM Neural Network for Large-Scale Text Classification},
  isbn={978-1-4673-8493-3},
  publisher={IEEE},
  address={Los Alamitos, CA},
  booktitle={15th IEEE International Conference on Data Mining Workshop : Proceedings ; 14–17 November 2015, Atlantic City, New Jersey},
  pages={847--854},
  editor={Cui, Peng},
  author={Benites, Fernando and Sapozhnikova, Elena}
}
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/123456789/33471">
    <dcterms:isPartOf rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/>
    <dc:creator>Benites, Fernando</dc:creator>
    <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2016-03-24T13:34:46Z</dc:date>
    <dcterms:title>HARAM : a Hierarchical ARAM Neural Network for Large-Scale Text Classification</dcterms:title>
    <dc:creator>Sapozhnikova, Elena</dc:creator>
    <void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/>
    <dc:language>eng</dc:language>
    <dcterms:abstract xml:lang="eng">With the rapid development of the Web, the need for text classification of large data volumes is permanently growing. Texts represented as bags-of-words possess usually very high dimensionality in the input space and often also in the output space if labeled with many categories. As a result, neural classifiers should be adapted to such large-scale data. We present here a well scalable extension to the fuzzy Adaptive Resonance Associative Map (ARAM) neural network which was specially developed for high-dimensional and large data. This extension aims at increasing the classification speed by adding an extra ART layer for clustering learned prototypes into large clusters. In this case the activation of all prototypes can be replaced by the activation of a small fraction of them, leading to a significant reduction of the classification time. This extension can be especially useful for multi-label classification tasks.</dcterms:abstract>
    <dc:contributor>Sapozhnikova, Elena</dc:contributor>
    <bibo:uri rdf:resource="https://kops.uni-konstanz.de/handle/123456789/33471"/>
    <foaf:homepage rdf:resource="http://localhost:8080/"/>
    <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2016-03-24T13:34:46Z</dcterms:available>
    <dc:contributor>Benites, Fernando</dc:contributor>
    <dspace:isPartOfCollection rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/>
    <dcterms:issued>2015-11</dcterms:issued>
  </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