Search (31 results, page 1 of 2)

  • × language_ss:"e"
  • × theme_ss:"Automatisches Klassifizieren"
  • × year_i:[2000 TO 2010}
  1. 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) 0.04 
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    Editor
    Gaul, W. u. G. Ritter
    Type
    m
  2. 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
  3. Reiner, U.: Automatic analysis of DDC notations (2007) 0.01 
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  4. Wu, M.; Fuller, M.; Wilkinson, R.: Using clustering and classification approaches in interactive retrieval (2001) 0.01 
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  5. Leroy, G.; Miller, T.; Rosemblat, G.; Browne, A.: ¬A balanced approach to health information evaluation : a vocabulary-based naïve Bayes classifier and readability formulas (2008) 0.01 
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  6. Hu, G.; Zhou, S.; Guan, J.; Hu, X.: Towards effective document clustering : a constrained K-means based approach (2008) 0.01 
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  7. 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
  8. Kanaan, G.; Al-Shalabi, R.; Ghwanmeh, S.; Al-Ma'adeed, H.: ¬A comparison of text-classification techniques applied to Arabic text (2009) 0.01 
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  9. 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
  10. Subramanian, S.; Shafer, K.E.: Clustering (2001) 0.01 
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    Date
    5. 5.2003 14:17:22
  11. Reiner, U.: DDC-based search in the data of the German National Bibliography (2008) 0.01 
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  12. Zhang, X: Rough set theory based automatic text categorization (2005) 0.00 
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    Type
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  13. Automatic classification research at OCLC (2002) 0.00 
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    Date
    5. 5.2003 9:22:09
  14. 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
  15. Yi, K.: Automatic text classification using library classification schemes : trends, issues and challenges (2007) 0.00 
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    22. 9.2008 18:31:54
  16. Chung, Y.-M.; Noh, Y.-H.: Developing a specialized directory system by automatically classifying Web documents (2003) 0.00 
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  17. Mukhopadhyay, S.; Peng, S.; Raje, R.; Palakal, M.; Mostafa, J.: Multi-agent information classification using dynamic acquaintance lists (2003) 0.00 
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  18. Wu, K.J.; Chen, M.-C.; Sun, Y.: Automatic topics discovery from hyperlinked documents (2004) 0.00 
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  19. Prabowo, R.; Jackson, M.; Burden, P.; Knoell, H.-D.: Ontology-based automatic classification for the Web pages : design, implementation and evaluation (2002) 0.00 
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  20. Duwairi, R.M.: Machine learning for Arabic text categorization (2006) 0.00 
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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.

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