Search (19 results, page 1 of 1)

  • × theme_ss:"Automatisches Klassifizieren"
  1. Hotho, A.; Bloehdorn, S.: Data Mining 2004 : Text classification by boosting weak learners based on terms and concepts (2004) 0.10
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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. Subramanian, S.; Shafer, K.E.: Clustering (2001) 0.02
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    Date
    5. 5.2003 14:17:22
  3. Groß, T.; Faden, M.: Automatische Indexierung elektronischer Dokumente an der Deutschen Zentralbibliothek für Wirtschaftswissenschaften : Bericht über die Jahrestagung der Internationalen Buchwissenschaftlichen Gesellschaft (2010) 0.02
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    Abstract
    Die zunehmende Verfügbarmachung digitaler Informationen in den letzten Jahren sowie die Aussicht auf ein weiteres Ansteigen der sogenannten Datenflut kumulieren in einem grundlegenden, sich weiter verstärkenden Informationsstrukturierungsproblem. Die stetige Zunahme von digitalen Informationsressourcen im World Wide Web sichert zwar jederzeit und ortsungebunden den Zugriff auf verschiedene Informationen; offen bleibt der strukturierte Zugang, insbesondere zu wissenschaftlichen Ressourcen. Angesichts der steigenden Anzahl elektronischer Inhalte und vor dem Hintergrund stagnierender bzw. knapper werdender personeller Ressourcen in der Sacherschließun schafft keine Bibliothek bzw. kein Bibliotheksverbund es mehr, weder aktuell noch zukünftig, alle digitalen Daten zu erfassen, zu strukturieren und zueinander in Beziehung zu setzen. In der Informationsgesellschaft des 21. Jahrhunderts wird es aber zunehmend wichtiger, die in der Flut verschwundenen wissenschaftlichen Informationen zeitnah, angemessen und vollständig zu strukturieren und somit als Basis für eine Wissensgenerierung wieder nutzbar zu machen. Eine normierte Inhaltserschließung digitaler Informationsressourcen ist deshalb für die Deutsche Zentralbibliothek für Wirtschaftswissenschaften (ZBW) als wichtige Informationsinfrastruktureinrichtung in diesem Bereich ein entscheidender und auch erfolgskritischer Aspekt im Wettbewerb mit anderen Informationsdienstleistern. Weil die traditionelle intellektuelle Sacherschließung aber nicht beliebig skalierbar ist - mit dem Anstieg der Zahl an Online-Dokumenten steigt proportional auch der personelle Ressourcenbedarf an Fachreferenten, wenn ein gewisser Qualitätsstandard gehalten werden soll - bedarf es zukünftig anderer Sacherschließungsverfahren. Automatisierte Verschlagwortungsmethoden werden dabei als einzige Möglichkeit angesehen, die bibliothekarische Sacherschließung auch im digitalen Zeitalter zukunftsfest auszugestalten. Zudem können maschinelle Ansätze dazu beitragen, die Heterogenitäten (Indexierungsinkonsistenzen) zwischen den einzelnen Sacherschließer zu nivellieren, und somit zu einer homogeneren Erschließung des Bibliotheksbestandes beitragen.
  4. Reiner, U.: Automatische DDC-Klassifizierung von bibliografischen Titeldatensätzen (2009) 0.02
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    Date
    22. 8.2009 12:54:24
  5. HaCohen-Kerner, Y. et al.: Classification using various machine learning methods and combinations of key-phrases and visual features (2016) 0.02
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    Date
    1. 2.2016 18:25:22
  6. Bock, H.-H.: Datenanalyse zur Strukturierung und Ordnung von Information (1989) 0.01
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    Pages
    S.1-22
  7. Dubin, D.: Dimensions and discriminability (1998) 0.01
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    Date
    22. 9.1997 19:16:05
  8. Automatic classification research at OCLC (2002) 0.01
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    Date
    5. 5.2003 9:22:09
  9. Jenkins, C.: Automatic classification of Web resources using Java and Dewey Decimal Classification (1998) 0.01
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    Date
    1. 8.1996 22:08:06
  10. Yoon, Y.; Lee, C.; Lee, G.G.: ¬An effective procedure for constructing a hierarchical text classification system (2006) 0.01
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    Date
    22. 7.2006 16:24:52
  11. Yi, K.: Automatic text classification using library classification schemes : trends, issues and challenges (2007) 0.01
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    Date
    22. 9.2008 18:31:54
  12. Liu, R.-L.: Context recognition for hierarchical text classification (2009) 0.01
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    Date
    22. 3.2009 19:11:54
  13. Pfeffer, M.: Automatische Vergabe von RVK-Notationen mittels fallbasiertem Schließen (2009) 0.01
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    Date
    22. 8.2009 19:51:28
  14. Zhu, W.Z.; Allen, R.B.: Document clustering using the LSI subspace signature model (2013) 0.01
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    Date
    23. 3.2013 13:22:36
  15. Egbert, J.; Biber, D.; Davies, M.: Developing a bottom-up, user-based method of web register classification (2015) 0.01
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    Date
    4. 8.2015 19:22:04
  16. Mengle, S.; Goharian, N.: Passage detection using text classification (2009) 0.01
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    Date
    22. 3.2009 19:14:43
  17. Liu, R.-L.: ¬A passage extractor for classification of disease aspect information (2013) 0.01
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    Date
    28.10.2013 19:22:57
  18. Khoo, C.S.G.; Ng, K.; Ou, S.: ¬An exploratory study of human clustering of Web pages (2003) 0.01
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    Date
    12. 9.2004 9:56:22
  19. Reiner, U.: Automatische DDC-Klassifizierung bibliografischer Titeldatensätze der Deutschen Nationalbibliografie (2009) 0.01
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    Date
    22. 1.2010 14:41:24