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  • × theme_ss:"Automatisches Klassifizieren"
  1. Reiner, U.: Automatische DDC-Klassifizierung von bibliografischen Titeldatensätzen (2009) 0.01 
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    Date
    22. 8.2009 12:54:24
  2. HaCohen-Kerner, Y. et al.: Classification using various machine learning methods and combinations of key-phrases and visual features (2016) 0.01 
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    Date
    1. 2.2016 18:25:22
  3. 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
  4. 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
  5. 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
  6. 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
  7. Bock, H.-H.: Datenanalyse zur Strukturierung und Ordnung von Information (1989) 0.00 
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    Pages
    S.1-22
  8. Dubin, D.: Dimensions and discriminability (1998) 0.00 
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    Date
    22. 9.1997 19:16:05
  9. Automatic classification research at OCLC (2002) 0.00 
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    Date
    5. 5.2003 9:22:09
  10. 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
  11. 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
  12. Oberhauser, O.: Automatisches Klassifizieren : Entwicklungsstand - Methodik - Anwendungsbereiche (2005) 0.00 
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    Footnote
    Rez. in: VÖB-Mitteilungen 58(2005) H.3, S.102-104 (R.F. Müller); ZfBB 53(2006) H.5, S.282-283 (L. Svensson): "Das Sammeln und Verzeichnen elektronischer Ressourcen gehört in wissenschaftlichen Bibliotheken längst zum Alltag. Parallel dazu kündigt sich ein Paradigmenwechsel bei den Findmitteln an: Um einen effizienten und benutzerorientierten Zugang zu den gemischten Kollektionen bieten zu können, experimentieren einige bibliothekarische Diensteanbieter wie z. B. das hbz (http://suchen.hbz-nrw.de/dreilaender/), die Bibliothek der North Carolina State University (www.lib.ncsu.edu/) und demnächst vascoda (www.vascoda.de/) und der Librarians-Internet Index (www.lii.org/) zunehmend mit Suchmaschinentechnologie. Dabei wird angestrebt, nicht nur einen vollinvertierten Suchindex anzubieten, sondern auch das Browsing durch eine hierarchisch geordnete Klassifikation. Von den Daten in den deutschen Verbunddatenbanken ist jedoch nur ein kleiner Teil schon klassifikatorisch erschlossen. Fremddaten aus dem angloamerikanischen Bereich sind oft mit LCC und/oder DDC erschlossen, wobei die Library of Congress sich bei der DDCErschließung auf Titel, die hauptsächlich für die Public Libraries interessant sind, konzentriert. Die Deutsche Nationalbibliothek wird ab 2007 Printmedien und Hochschulschriften flächendeckend mit DDC erschließen. Es ist aber schon offensichtlich, dass v. a. im Bereich der elektronischen Publikationen die anfallenden Dokumentenmengen mit immer knapperen Personalressourcen nicht intellektuell erschlossen werden können, sondern dass neue Verfahren entwickelt werden müssen. Hier kommt Oberhausers Buch gerade richtig. Seit Anfang der 1990er Jahre sind mehrere Projekte zum Thema automatisches Klassifizieren durchgeführt worden. Wer sich in diese Thematik einarbeiten wollte oder sich für die Ergebnisse der größeren Projekte interessierte, konnte bislang auf keine Überblicksdarstellung zurückgreifen, sondern war auf eine Vielzahl von Einzeluntersuchungen sowie die Projektdokumentationen angewiesen. Oberhausers Darstellung, die auf einer Fülle von publizierter und grauer Literatur fußt, schließt diese Lücke. Das selbst gesetzte Ziel, einen guten Überblick über den momentanen Kenntnisstand und die Ergebnisse der einschlägigen Projekte verständlich zu vermitteln, erfüllt der Autor mit Bravour. Dabei ist anzumerken, dass er ein bibliothekarisches Grundwissen und mindestens grundlegende Kenntnisse über informationswissenschaftliche Grundbegriffe und Fragestellungen voraussetzt, wobei hier für den Einsteiger einige Hinweise auf einführende Darstellungen wünschenswert gewesen wären.
  13. Drori, O.; Alon, N.: Using document classification for displaying search results (2003) 0.00 
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    Source
    Journal of information science. 29(2003) no.2, S.97-106
  14. Chung, Y.-M.; Noh, Y.-H.: Developing a specialized directory system by automatically classifying Web documents (2003) 0.00 
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    Journal of information science. 29(2003) no.2, S.117-126
  15. Liu, X.; Yu, S.; Janssens, F.; Glänzel, W.; Moreau, Y.; Moor, B.de: Weighted hybrid clustering by combining text mining and bibliometrics on a large-scale journal database (2010) 0.00 
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    1. 6.2010 9:29:57
  16. Sommer, M.: Automatische Generierung von DDC-Notationen für Hochschulveröffentlichungen (2012) 0.00 
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    Date
    29. 1.2013 15:44:43
  17. Liu, R.-L.: Context recognition for hierarchical text classification (2009) 0.00 
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    Date
    22. 3.2009 19:11:54
  18. Zhu, W.Z.; Allen, R.B.: Document clustering using the LSI subspace signature model (2013) 0.00 
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    Date
    23. 3.2013 13:22:36
  19. Egbert, J.; Biber, D.; Davies, M.: Developing a bottom-up, user-based method of web register classification (2015) 0.00 
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    Date
    4. 8.2015 19:22:04
  20. Ribeiro-Neto, B.; Laender, A.H.F.; Lima, L.R.S. de: ¬An experimental study in automatically categorizing medical documents (2001) 0.00 
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    Date
    29. 9.2001 13:59:42

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