Search (33 results, page 1 of 2)

  • × theme_ss:"Automatisches Klassifizieren"
  1. Yi, K.: Automatic text classification using library classification schemes : trends, issues and challenges (2007) 0.08 
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    Date
    22. 9.2008 18:31:54
    Source
    International cataloguing and bibliographic control. 36(2007) no.4, S.78-82
    Year
    2007
  2. Hotho, A.; Bloehdorn, S.: Data Mining 2004 : Text classification by boosting weak learners based on terms and concepts (2004) 0.07 
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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
  3. Pfeffer, M.: Automatische Vergabe von RVK-Notationen mittels fallbasiertem Schließen (2009) 0.06 
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    Date
    22. 8.2009 19:51:28
    Footnote
    Vgl. auch die Präsentationen unter: http://www.bibliothek.uni-regensburg.de/Systematik/pdf/Anw2008_PPT1.pdf. http://blog.bib.uni-mannheim.de/Classification/wp-content/uploads/2007/10/hu-berlin-2007-2.pdf. Volltexte unter:
  4. Reiner, U.: Automatische DDC-Klassifizierung bibliografischer Titeldatensätze der Deutschen Nationalbibliografie (2009) 0.04 
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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.
    Date
    22. 1.2010 14:41:24
  5. Reiner, U.: Automatic analysis of DDC notations (2007) 0.04 
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    Year
    2007
  6. Hung, C.-M.; Chien, L.-F.: Web-based text classification in the absence of manually labeled training documents (2007) 0.02 
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    Source
    Journal of the American Society for Information Science and Technology. 58(2007) no.1, S.88-96
    Year
    2007
  7. Liu, R.-L.: Dynamic category profiling for text filtering and classification (2007) 0.02 
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    Source
    Information processing and management. 43(2007) no.1, S.154-168
    Year
    2007
  8. Yoon, Y.; Lee, G.G.: Efficient implementation of associative classifiers for document classification (2007) 0.02 
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    Source
    Information processing and management. 43(2007) no.2, S.393-405
    Year
    2007
  9. Hagedorn, K.; Chapman, S.; Newman, D.: Enhancing search and browse using automated clustering of subject metadata (2007) 0.02 
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    Source
    D-Lib magazine. 13(2007) nos.7/8, x S
    Year
    2007
  10. Golub, K.; Hamon, T.; Ardö, A.: Automated classification of textual documents based on a controlled vocabulary in engineering (2007) 0.02 
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    Source
    Knowledge organization. 34(2007) no.4, S.247-263
    Year
    2007
  11. Li, T.; Zhu, S.; Ogihara, M.: Hierarchical document classification using automatically generated hierarchy (2007) 0.02 
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    Source
    Journal of intelligent information systems. 29(2007) no.2, S.211-230
    Year
    2007
  12. Cathey, R.J.; Jensen, E.C.; Beitzel, S.M.; Frieder, O.; Grossman, D.: Exploiting parallelism to support scalable hierarchical clustering (2007) 0.02 
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    Source
    Journal of the American Society for Information Science and Technology. 58(2007) no.8, S.1207-1221
    Year
    2007
  13. Peng, F.; Huang, X.: Machine learning for Asian language text classification (2007) 0.02 
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    Source
    Journal of documentation. 63(2007) no.3, S.378-397
    Year
    2007
  14. Pfeffer, M.: Automatische Vergabe von RVK-Notationen anhand von bibliografischen Daten mittels fallbasiertem Schließen (2007) 0.02 
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    Year
    2007
  15. Helmbrecht-Schaar, A.: Entwicklung eines Verfahrens der automatischen Klassifizierung für Textdokumente aus dem Fachbereich Informatik mithilfe eines fachspezifischen Klassifikationssystems (2007) 0.02 
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    Year
    2007
  16. Puzicha, J.: Informationen finden! : Intelligente Suchmaschinentechnologie & automatische Kategorisierung (2007) 0.02 
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    Year
    2007
  17. Subramanian, S.; Shafer, K.E.: Clustering (2001) 0.02 
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    Date
    5. 5.2003 14:17:22
  18. Reiner, U.: Automatische DDC-Klassifizierung von bibliografischen Titeldatensätzen (2009) 0.02 
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    Date
    22. 8.2009 12:54:24
  19. 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
  20. Kasprzik, A.: Automatisierte und semiautomatisierte Klassifizierung : eine Analyse aktueller Projekte (2014) 0.02 
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    Abstract
    Das sprunghafte Anwachsen der Menge digital verfügbarer Dokumente gepaart mit dem Zeit- und Personalmangel an wissenschaftlichen Bibliotheken legt den Einsatz von halb- oder vollautomatischen Verfahren für die verbale und klassifikatorische Inhaltserschließung nahe. Nach einer kurzen allgemeinen Einführung in die gängige Methodik beleuchtet dieser Artikel eine Reihe von Projekten zur automatisierten Klassifizierung aus dem Zeitraum 2007-2012 und aus dem deutschsprachigen Raum. Ein Großteil der vorgestellten Projekte verwendet Methoden des Maschinellen Lernens aus der Künstlichen Intelligenz, arbeitet meist mit angepassten Versionen einer kommerziellen Software und bezieht sich in der Regel auf die Dewey Decimal Classification (DDC). Als Datengrundlage dienen Metadatensätze, Abstracs, Inhaltsverzeichnisse und Volltexte in diversen Datenformaten. Die abschließende Analyse enthält eine Anordnung der Projekte nach einer Reihe von verschiedenen Kriterien und eine Zusammenfassung der aktuellen Lage und der größten Herausfordungen für automatisierte Klassifizierungsverfahren.

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