Search (38 results, page 1 of 2)

  • × language_ss:"e"
  • × theme_ss:"Automatisches Klassifizieren"
  1. Hotho, A.; Bloehdorn, S.: Data Mining 2004 : Text classification by boosting weak learners based on terms and concepts (2004) 0.03
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    Content
    Vgl.: http://www.google.de/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&ved=0CEAQFjAA&url=http%3A%2F%2Fciteseerx.ist.psu.edu%2Fviewdoc%2Fdownload%3Fdoi%3D10.1.1.91.4940%26rep%3Drep1%26type%3Dpdf&ei=dOXrUMeIDYHDtQahsIGACg&usg=AFQjCNHFWVh6gNPvnOrOS9R3rkrXCNVD-A&sig2=5I2F5evRfMnsttSgFF9g7Q&bvm=bv.1357316858,d.Yms.
    Date
    8. 1.2013 10:22:32
  2. Zhang, X: Rough set theory based automatic text categorization (2005) 0.02
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    Abstract
    Der Forschungsbericht "Rough Set Theory Based Automatic Text Categorization and the Handling of Semantic Heterogeneity" von Xueying Zhang ist in Buchform auf Englisch erschienen. Zhang hat in ihrer Arbeit ein Verfahren basierend auf der Rough Set Theory entwickelt, das Beziehungen zwischen Schlagwörtern verschiedener Vokabulare herstellt. Sie war von 2003 bis 2005 Mitarbeiterin des IZ und ist seit Oktober 2005 Associate Professor an der Nanjing University of Science and Technology.
  3. Koch, T.; Vizine-Goetz, D.: DDC and knowledge organization in the digital library : Research and development. Demonstration pages (1999) 0.02
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    Abstract
    Der Workshop gibt einen Einblick in die aktuelle Forschung und Entwicklung zur Wissensorganisation in digitalen Bibliotheken. Diane Vizine-Goetz vom OCLC Office of Research in Dublin, Ohio, stellt die Forschungsprojekte von OCLC zur Anpassung und Weiterentwicklung der Dewey Decimal Classification als Wissensorganisationsinstrument fuer grosse digitale Dokumentensammlungen vor. Traugott Koch, NetLab, Universität Lund in Schweden, demonstriert die Ansätze und Lösungen des EU-Projekts DESIRE zum Einsatz von intellektueller und vor allem automatischer Klassifikation in Fachinformationsdiensten im Internet.
  4. Chan, L.M.; Lin, X.; Zeng, M.: Structural and multilingual approaches to subject access on the Web (1999) 0.01
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    Abstract
    Zu den großen Herausforderungen einer sinnvollen Suche im WWW gehören die riesige Menge des Verfügbaren und die Sparchbarrieren. Verfahren, die die Web-Ressourcen im Hinblick auf ein effizienteres Retrieval inhaltlich strukturieren, werden daher ebenso dringend benötigt wie Programme, die mit der Sprachvielfalt umgehen können. Im folgenden Vortrag werden wir einige Ansätze diskutieren, die zur Bewältigung der beiden Probleme derzeit unternommen werden
  5. Sojka, P.; Lee, M.; Rehurek, R.; Hatlapatka, R.; Kucbel, M.; Bouche, T.; Goutorbe, C.; Anghelache, R.; Wojciechowski, K.: Toolset for entity and semantic associations : Final Release (2013) 0.01
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    Field
    Mathematik
    Object
    Zentralblatt für Mathematik
  6. Reiner, U.: Automatic analysis of DDC notations (2007) 0.01
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    Content
    Vortrag während der EDUG-Konferenz der European DDC users' group am 11.06.2007 in Bern.
  7. Barthel, S.; Tönnies, S.; Balke, W.-T.: Large-scale experiments for mathematical document classification (2013) 0.01
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    Field
    Mathematik
    Object
    Zentralblatt für Mathematik
  8. Vizine-Goetz, D.: NetLab / OCLC collaboration seeks to improve Web searching (1999) 0.01
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    Abstract
    Vorstellung verschiedener Projekte zur Verbesserung der Internet-Erschließung mit Hilfe der DDC
  9. Subramanian, S.; Shafer, K.E.: Clustering (2001) 0.00
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    Date
    5. 5.2003 14:17:22
  10. HaCohen-Kerner, Y. et al.: Classification using various machine learning methods and combinations of key-phrases and visual features (2016) 0.00
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    Date
    1. 2.2016 18:25:22
  11. Lindholm, J.; Schönthal, T.; Jansson , K.: Experiences of harvesting Web resources in engineering using automatic classification (2003) 0.00
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    Footnote
    Auch unter: http://www.ariadne.ac.uk/issue37/lindholm/ und http://engine-e.lub.lu.se/
  12. Savic, D.: Designing an expert system for classifying office documents (1994) 0.00
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    Source
    Records management quarterly. 28(1994) no.3, S.20-29
  13. Savic, D.: Automatic classification of office documents : review of available methods and techniques (1995) 0.00
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    Source
    Records management quarterly. 29(1995) no.4, S.3-18
  14. Ruocco, A.S.; Frieder, O.: Clustering and classification of large document bases in a parallel environment (1997) 0.00
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    Date
    29. 7.1998 17:45:02
  15. Ruiz, M.E.; Srinivasan, P.: Combining machine learning and hierarchical indexing structures for text categorization (2001) 0.00
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    Date
    11. 5.2003 18:29:44
  16. Dubin, D.: Dimensions and discriminability (1998) 0.00
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    Date
    22. 9.1997 19:16:05
  17. Automatic classification research at OCLC (2002) 0.00
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    Date
    5. 5.2003 9:22:09
  18. Jenkins, C.: Automatic classification of Web resources using Java and Dewey Decimal Classification (1998) 0.00
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    Date
    1. 8.1996 22:08:06
  19. Yoon, Y.; Lee, C.; Lee, G.G.: ¬An effective procedure for constructing a hierarchical text classification system (2006) 0.00
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    Date
    22. 7.2006 16:24:52
  20. Yi, K.: Automatic text classification using library classification schemes : trends, issues and challenges (2007) 0.00
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    Date
    22. 9.2008 18:31:54