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  • × theme_ss:"Automatisches Klassifizieren"
  1. Chan, L.M.; Lin, X.; Zeng, M.L.: Structural and multilingual approaches to subject access on the Web (2000) 0.04
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  2. Panyr, J.: Automatische Klassifikation und Information Retrieval : Anwendung und Entwicklung komplexer Verfahren in Information-Retrieval-Systemen und ihre Evaluierung (1986) 0.04
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    Footnote
    Zugleich Dissertation U Saarbrücken 1085
    Type
    m
  3. Reiner, U.: Automatische DDC-Klassifizierung von bibliografischen Titeldatensätzen (2009) 0.03
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    Date
    22. 8.2009 12:54:24
  4. 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
  5. Reiner, U.: Automatische DDC-Klassifizierung bibliografischer Titeldatensätze der Deutschen Nationalbibliografie (2009) 0.02
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    Abstract
    Das Klassifizieren von Objekten (z. B. Fauna, Flora, Texte) ist ein Verfahren, das auf menschlicher Intelligenz basiert. In der Informatik - insbesondere im Gebiet der Künstlichen Intelligenz (KI) - wird u. a. untersucht, inweit Verfahren, die menschliche Intelligenz benötigen, automatisiert werden können. Hierbei hat sich herausgestellt, dass die Lösung von Alltagsproblemen eine größere Herausforderung darstellt, als die Lösung von Spezialproblemen, wie z. B. das Erstellen eines Schachcomputers. So ist "Rybka" der seit Juni 2007 amtierende Computerschach-Weltmeistern. Inwieweit Alltagsprobleme mit Methoden der Künstlichen Intelligenz gelöst werden können, ist eine - für den allgemeinen Fall - noch offene Frage. Beim Lösen von Alltagsproblemen spielt die Verarbeitung der natürlichen Sprache, wie z. B. das Verstehen, eine wesentliche Rolle. Den "gesunden Menschenverstand" als Maschine (in der Cyc-Wissensbasis in Form von Fakten und Regeln) zu realisieren, ist Lenat's Ziel seit 1984. Bezüglich des KI-Paradeprojektes "Cyc" gibt es CycOptimisten und Cyc-Pessimisten. Das Verstehen der natürlichen Sprache (z. B. Werktitel, Zusammenfassung, Vorwort, Inhalt) ist auch beim intellektuellen Klassifizieren von bibliografischen Titeldatensätzen oder Netzpublikationen notwendig, um diese Textobjekte korrekt klassifizieren zu können. Seit dem Jahr 2007 werden von der Deutschen Nationalbibliothek nahezu alle Veröffentlichungen mit der Dewey Dezimalklassifikation (DDC) intellektuell klassifiziert.
    Die Menge der zu klassifizierenden Veröffentlichungen steigt spätestens seit der Existenz des World Wide Web schneller an, als sie intellektuell sachlich erschlossen werden kann. Daher werden Verfahren gesucht, um die Klassifizierung von Textobjekten zu automatisieren oder die intellektuelle Klassifizierung zumindest zu unterstützen. Seit 1968 gibt es Verfahren zur automatischen Dokumentenklassifizierung (Information Retrieval, kurz: IR) und seit 1992 zur automatischen Textklassifizierung (ATC: Automated Text Categorization). Seit immer mehr digitale Objekte im World Wide Web zur Verfügung stehen, haben Arbeiten zur automatischen Textklassifizierung seit ca. 1998 verstärkt zugenommen. Dazu gehören seit 1996 auch Arbeiten zur automatischen DDC-Klassifizierung bzw. RVK-Klassifizierung von bibliografischen Titeldatensätzen und Volltextdokumenten. Bei den Entwicklungen handelt es sich unseres Wissens bislang um experimentelle und keine im ständigen Betrieb befindlichen Systeme. Auch das VZG-Projekt Colibri/DDC ist seit 2006 u. a. mit der automatischen DDC-Klassifizierung befasst. Die diesbezüglichen Untersuchungen und Entwicklungen dienen zur Beantwortung der Forschungsfrage: "Ist es möglich, eine inhaltlich stimmige DDC-Titelklassifikation aller GVK-PLUS-Titeldatensätze automatisch zu erzielen?"
    Date
    22. 1.2010 14:41:24
  6. Pfeffer, M.: Automatische Vergabe von RVK-Notationen mittels fallbasiertem Schließen (2009) 0.02
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    Date
    22. 8.2009 19:51:28
    Imprint
    Frankfurt, M. : Klostermann
  7. Classification, automation, and new media : Proceedings of the 24th Annual Conference of the Gesellschaft für Klassifikation e.V., University of Passau, March 15 - 17, 2000 (2002) 0.01
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    Editor
    Gaul, W. u. G. Ritter
    Type
    m
  8. Schek, M.: Automatische Klassifizierung in Erschließung und Recherche eines Pressearchivs (2006) 0.01
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    Source
    Spezialbibliotheken zwischen Auftrag und Ressourcen: 6.-9. September 2005 in München, 30. Arbeits- und Fortbildungstagung der ASpB e.V. / Sektion 5 im Deutschen Bibliotheksverband. Red.: M. Brauer
    Theme
    Semantisches Umfeld in Indexierung u. Retrieval
  9. 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
  10. Reiner, U.: Automatic analysis of DDC notations (2007) 0.01
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  11. Wu, M.; Fuller, M.; Wilkinson, R.: Using clustering and classification approaches in interactive retrieval (2001) 0.01
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  12. Schek, M.: Automatische Klassifizierung und Visualisierung im Archiv der Süddeutschen Zeitung (2005) 0.01
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    Theme
    Semantisches Umfeld in Indexierung u. Retrieval
  13. Wätjen, H.-J.; Diekmann, B.; Möller, G.; Carstensen, K.-U.: Bericht zum DFG-Projekt: GERHARD : German Harvest Automated Retrieval and Directory (1998) 0.01
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  14. Bock, H.-H.: Automatische Klassifikation : theoretische und praktische Methoden zur Gruppierung und Strukturierung von Daten (Cluster-Analyse) (1974) 0.01
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    Type
    m
  15. 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
    Source
    Challenges in knowledge representation and organization for the 21st century: Integration of knowledge across boundaries. Proceedings of the 7th ISKO International Conference Granada, Spain, July 10-13, 2002. Ed.: M. López-Huertas
  16. Schulze, U.: Erfahrungen bei der Anwendung automatischer Klassifizierungsverfahren zur Inhaltsanalyse einer Dokumentenmenge (1978) 0.01
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  17. Pfister, J.: Clustering von Patent-Dokumenten am Beispiel der Datenbanken des Fachinformationszentrums Karlsruhe (2006) 0.01
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    Source
    Effektive Information Retrieval Verfahren in Theorie und Praxis: ausgewählte und erweiterte Beiträge des Vierten Hildesheimer Evaluierungs- und Retrievalworkshop (HIER 2005), Hildesheim, 20.7.2005. Hrsg.: T. Mandl u. C. Womser-Hacker
  18. Oberhauser, O.: Automatisches Klassifizieren : Entwicklungsstand - Methodik - Anwendungsbereiche (2005) 0.01
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    Theme
    Grundlagen u. Einführungen: Allgemeine Literatur
    Type
    m
  19. Ruiz, M.E.; Srinivasan, P.: Combining machine learning and hierarchical indexing structures for text categorization (2001) 0.01
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    Source
    Advances in classification research, vol.10: proceedings of the 10th ASIS SIG/CR Classification Research Workshop. Ed.: Albrechtsen, H. u. J.E. Mai
  20. Krellenstein, M.: Document classification at Northern Light (1999) 0.01
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  • el 11
  • m 5
  • x 3
  • r 2
  • s 2
  • d 1
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