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  • × theme_ss:"Automatisches Klassifizieren"
  1. Hotho, A.; Bloehdorn, S.: Data Mining 2004 : Text classification by boosting weak learners based on terms and concepts (2004) 0.40 
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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. Chae, G.; Park, J.; Park, J.; Yeo, W.S.; Shi, C.: Linking and clustering artworks using social tags : revitalizing crowd-sourced information on cultural collections (2016) 0.01 
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    Field
    Kunst
  3. Subramanian, S.; Shafer, K.E.: Clustering (2001) 0.00 
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    Date
    5. 5.2003 14:17:22
  4. Ardö, A.; Koch, T.: Automatic classification applied to full-text Internet documents in a robot-generated subject index (1999) 0.00 
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  5. Reiner, U.: Automatische DDC-Klassifizierung von bibliografischen Titeldatensätzen (2009) 0.00 
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    Date
    22. 8.2009 12:54:24
  6. HaCohen-Kerner, Y. et al.: Classification using various machine learning methods and combinations of key-phrases and visual features (2016) 0.00 
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    Date
    1. 2.2016 18:25:22
  7. Braun, T.: Dokumentklassifikation durch Clustering (o.J.) 0.00 
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  8. Liu, R.-L.: Context-based term frequency assessment for text classification (2010) 0.00 
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    Abstract
    Automatic text classification (TC) is essential for the management of information. To properly classify a document d, it is essential to identify the semantics of each term t in d, while the semantics heavily depend on context (neighboring terms) of t in d. Therefore, we present a technique CTFA (Context-based Term Frequency Assessment) that improves text classifiers by considering term contexts in test documents. The results of the term context recognition are used to assess term frequencies of terms, and hence CTFA may easily work with various kinds of text classifiers that base their TC decisions on term frequencies, without needing to modify the classifiers. Moreover, CTFA is efficient, and neither huge memory nor domain-specific knowledge is required. Empirical results show that CTFA successfully enhances performance of several kinds of text classifiers on different experimental data.
  9. Bock, H.-H.: Datenanalyse zur Strukturierung und Ordnung von Information (1989) 0.00 
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    Pages
    S.1-22
  10. Dubin, D.: Dimensions and discriminability (1998) 0.00 
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    Date
    22. 9.1997 19:16:05
  11. Automatic classification research at OCLC (2002) 0.00 
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    Date
    5. 5.2003 9:22:09
  12. Jenkins, C.: Automatic classification of Web resources using Java and Dewey Decimal Classification (1998) 0.00 
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    Date
    1. 8.1996 22:08:06
  13. 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
  14. Yi, K.: Automatic text classification using library classification schemes : trends, issues and challenges (2007) 0.00 
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    Date
    22. 9.2008 18:31:54
  15. Koch, T.: Nutzung von Klassifikationssystemen zur verbesserten Beschreibung, Organisation und Suche von Internetressourcen (1998) 0.00 
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  16. Koch, T.; Ardö, A.: Automatic classification of full-text HTML-documents from one specific subject area : DESIRE II D3.6a, Working Paper 2 (2000) 0.00 
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  17. Brückner, T.; Dambeck, H.: Sortierautomaten : Grundlagen der Textklassifizierung (2003) 0.00 
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  18. Lindholm, J.; Schönthal, T.; Jansson , K.: Experiences of harvesting Web resources in engineering using automatic classification (2003) 0.00 
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  19. Pfister, J.: Clustering von Patent-Dokumenten am Beispiel der Datenbanken des Fachinformationszentrums Karlsruhe (2006) 0.00 
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    Source
    Effektive Information Retrieval Verfahren in Theorie und Praxis: ausgewählte und erweiterte Beiträge des Vierten Hildesheimer Evaluierungs- und Retrievalworkshop (HIER 2005), Hildesheim, 20.7.2005. Hrsg.: T. Mandl u. C. Womser-Hacker
  20. Jersek, T.: Automatische DDC-Klassifizierung mit Lingo : Vorgehensweise und Ergebnisse (2012) 0.00 
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