Search (12 results, page 1 of 1)

  • × theme_ss:"Automatisches Klassifizieren"
  • × type_ss:"a"
  • × year_i:[2000 TO 2010}
  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. Yi, K.: Automatic text classification using library classification schemes : trends, issues and challenges (2007) 0.05 
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    Date
    22. 9.2008 18:31:54
    Source
    International cataloguing and bibliographic control. 36(2007) no.4, S.78-82
  3. Wang, J.: ¬An extensive study on automated Dewey Decimal Classification (2009) 0.02 
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    Abstract
    In this paper, we present a theoretical analysis and extensive experiments on the automated assignment of Dewey Decimal Classification (DDC) classes to bibliographic data with a supervised machine-learning approach. Library classification systems, such as the DDC, impose great obstacles on state-of-art text categorization (TC) technologies, including deep hierarchy, data sparseness, and skewed distribution. We first analyze statistically the document and category distributions over the DDC, and discuss the obstacles imposed by bibliographic corpora and library classification schemes on TC technology. To overcome these obstacles, we propose an innovative algorithm to reshape the DDC structure into a balanced virtual tree by balancing the category distribution and flattening the hierarchy. To improve the classification effectiveness to a level acceptable to real-world applications, we propose an interactive classification model that is able to predict a class of any depth within a limited number of user interactions. The experiments are conducted on a large bibliographic collection created by the Library of Congress within the science and technology domains over 10 years. With no more than three interactions, a classification accuracy of nearly 90% is achieved, thus providing a practical solution to the automatic bibliographic classification problem.
  4. Subramanian, S.; Shafer, K.E.: Clustering (2001) 0.02 
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    Date
    5. 5.2003 14:17:22
  5. Guerrero-Bote, V.P.; Moya Anegón, F. de; Herrero Solana, V.: Document organization using Kohonen's algorithm (2002) 0.02 
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    Abstract
    The classification of documents from a bibliographic database is a task that is linked to processes of information retrieval based on partial matching. A method is described of vectorizing reference documents from LISA which permits their topological organization using Kohonen's algorithm. As an example a map is generated of 202 documents from LISA, and an analysis is made of the possibilities of this type of neural network with respect to the development of information retrieval systems based on graphical browsing.
  6. Reiner, U.: DDC-based search in the data of the German National Bibliography (2008) 0.01 
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    Abstract
    In 2004, the German National Library began to classify title records of the German National Bibliography according to subject groups based on the divisions of the Dewey Decimal Classification (DDC). Since 2006, all titles of the main series of the German National Bibliography are classified in strict compliance with the DDC. On this basis, an enhanced DDC-based search can be realized - e.g., searching the data of the German National Bibliography for title records using number components of synthesized classification numbers or searching for DDC numbers using unclassified title records. This paper gives an account of the current research and development of the DDC-based search. The work is conducted in the VZG project Colibri that focuses on the automatic analysis of DDC-synthesized numbers and the automatic classification of bibliographic title records.
  7. 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
  8. Liu, R.-L.: Context recognition for hierarchical text classification (2009) 0.01 
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    Date
    22. 3.2009 19:11:54
  9. Pfeffer, M.: Automatische Vergabe von RVK-Notationen mittels fallbasiertem Schließen (2009) 0.01 
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    Date
    22. 8.2009 19:51:28
  10. Mengle, S.; Goharian, N.: Passage detection using text classification (2009) 0.01 
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    Date
    22. 3.2009 19:14:43
  11. 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
  12. Reiner, U.: Automatische DDC-Klassifizierung bibliografischer Titeldatensätze der Deutschen Nationalbibliografie (2009) 0.01 
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    Date
    22. 1.2010 14:41:24

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