Search (60 results, page 2 of 3)

  • × theme_ss:"Automatisches Klassifizieren"
  1. Bock, H.-H.: Datenanalyse zur Strukturierung und Ordnung von Information (1989) 0.01 
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    Pages
    S.1-22
  2. Dubin, D.: Dimensions and discriminability (1998) 0.01 
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    Date
    22. 9.1997 19:16:05
  3. Automatic classification research at OCLC (2002) 0.01 
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    Date
    5. 5.2003 9:22:09
  4. 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
  5. 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
  6. 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
  7. Mostafa, J.; Quiroga, L.M.; Palakal, M.: Filtering medical documents using automated and human classification methods (1998) 0.01 
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  8. Koch, T.; Vizine-Goetz, D.: DDC and knowledge organization in the digital library : Research and development. Demonstration pages (1999) 0.01 
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    Footnote
    Vortrag anläßlich des Workshops am 21.10.1999, Deutsche Bibliothek, Frankfurt/M.
  9. Chung, Y.-M.; Noh, Y.-H.: Developing a specialized directory system by automatically classifying Web documents (2003) 0.01 
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  10. Mukhopadhyay, S.; Peng, S.; Raje, R.; Palakal, M.; Mostafa, J.: Multi-agent information classification using dynamic acquaintance lists (2003) 0.01 
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  11. Wu, K.J.; Chen, M.-C.; Sun, Y.: Automatic topics discovery from hyperlinked documents (2004) 0.01 
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  12. Prabowo, R.; Jackson, M.; Burden, P.; Knoell, H.-D.: Ontology-based automatic classification for the Web pages : design, implementation and evaluation (2002) 0.01 
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  13. Duwairi, R.M.: Machine learning for Arabic text categorization (2006) 0.01 
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    Abstract
    In this article we propose a distance-based classifier for categorizing Arabic text. Each category is represented as a vector of words in an m-dimensional space, and documents are classified on the basis of their closeness to feature vectors of categories. The classifier, in its learning phase, scans the set of training documents to extract features of categories that capture inherent category-specific properties; in its testing phase the classifier uses previously determined category-specific features to categorize unclassified documents. Stemming was used to reduce the dimensionality of feature vectors of documents. The accuracy of the classifier was tested by carrying out several categorization tasks on an in-house collected Arabic corpus. The results show that the proposed classifier is very accurate and robust.
  14. Hung, C.-M.; Chien, L.-F.: Web-based text classification in the absence of manually labeled training documents (2007) 0.01 
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  15. Pfeffer, M.: Automatische Vergabe von RVK-Notationen anhand von bibliografischen Daten mittels fallbasiertem Schließen (2007) 0.01 
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  16. Malenica, M.; Smuc, T.; Snajder, J.; Basic, B.D.: Language morphology offset : text classification on a Croatian-English parallel corpus (2008) 0.01 
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  17. Zhou, G.D.; Zhang, M.; Ji, D.H.; Zhu, Q.M.: Hierarchical learning strategy in semantic relation extraction (2008) 0.01 
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  18. Montesi, M.; Navarrete, T.: Classifying web genres in context : A case study documenting the web genres used by a software engineer (2008) 0.01 
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  19. Li, T.; Zhu, S.; Ogihara, M.: Hierarchical document classification using automatically generated hierarchy (2007) 0.01 
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  20. Sommer, M.: Automatische Generierung von DDC-Notationen für Hochschulveröffentlichungen (2012) 0.01 
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