Search (36 results, page 1 of 2)

  • × language_ss:"e"
  • × theme_ss:"Automatisches Klassifizieren"
  • × type_ss:"a"
  1. 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
  2. Egbert, J.; Biber, D.; Davies, M.: Developing a bottom-up, user-based method of web register classification (2015) 0.03 
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    Date
    4. 8.2015 19:22:04
  3. Khoo, C.S.G.; Ng, K.; Ou, S.: ¬An exploratory study of human clustering of Web pages (2003) 0.02 
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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
  4. Subramanian, S.; Shafer, K.E.: Clustering (2001) 0.02 
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    Date
    5. 5.2003 14:17:22
  5. 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
  6. Wu, M.; Fuller, M.; Wilkinson, R.: Using clustering and classification approaches in interactive retrieval (2001) 0.02 
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  7. Dubin, D.: Dimensions and discriminability (1998) 0.01 
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    Date
    22. 9.1997 19:16:05
  8. 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
  9. 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
  10. 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
  11. Liu, R.-L.: Context recognition for hierarchical text classification (2009) 0.01 
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    Date
    22. 3.2009 19:11:54
  12. Zhu, W.Z.; Allen, R.B.: Document clustering using the LSI subspace signature model (2013) 0.01 
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    Date
    23. 3.2013 13:22:36
  13. Mengle, S.; Goharian, N.: Passage detection using text classification (2009) 0.01 
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    Date
    22. 3.2009 19:14:43
  14. Liu, R.-L.: ¬A passage extractor for classification of disease aspect information (2013) 0.01 
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    Date
    28.10.2013 19:22:57
  15. Ahmed, M.; Mukhopadhyay, M.; Mukhopadhyay, P.: Automated knowledge organization : AI ML based subject indexing system for libraries (2023) 0.01 
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  16. Mostafa, J.; Quiroga, L.M.; Palakal, M.: Filtering medical documents using automated and human classification methods (1998) 0.00 
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  17. Chung, Y.-M.; Noh, Y.-H.: Developing a specialized directory system by automatically classifying Web documents (2003) 0.00 
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  18. 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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  19. Wu, K.J.; Chen, M.-C.; Sun, Y.: Automatic topics discovery from hyperlinked documents (2004) 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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