Search (200 results, page 1 of 10)

  • × theme_ss:"Automatisches Klassifizieren"
  1. Hotho, A.; Bloehdorn, S.: Data Mining 2004 : Text classification by boosting weak learners based on terms and concepts (2004) 0.11 
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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
    Pages
    S.331-334
  2. Subramanian, S.; Shafer, K.E.: Clustering (2001) 0.05 
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    Date
    5. 5.2003 14:17:22
    Source
    Journal of library administration. 34(2001) nos.3/4, S.221-228
  3. HaCohen-Kerner, Y. et al.: Classification using various machine learning methods and combinations of key-phrases and visual features (2016) 0.04 
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    Date
    1. 2.2016 18:25:22
    Pages
    S.64-75
  4. Golub, K.: Automated subject classification of textual web documents (2006) 0.03 
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    Abstract
    Purpose - To provide an integrated perspective to similarities and differences between approaches to automated classification in different research communities (machine learning, information retrieval and library science), and point to problems with the approaches and automated classification as such. Design/methodology/approach - A range of works dealing with automated classification of full-text web documents are discussed. Explorations of individual approaches are given in the following sections: special features (description, differences, evaluation), application and characteristics of web pages. Findings - Provides major similarities and differences between the three approaches: document pre-processing and utilization of web-specific document characteristics is common to all the approaches; major differences are in applied algorithms, employment or not of the vector space model and of controlled vocabularies. Problems of automated classification are recognized. Research limitations/implications - The paper does not attempt to provide an exhaustive bibliography of related resources. Practical implications - As an integrated overview of approaches from different research communities with application examples, it is very useful for students in library and information science and computer science, as well as for practitioners. Researchers from one community have the information on how similar tasks are conducted in different communities. Originality/value - To the author's knowledge, no review paper on automated text classification attempted to discuss more than one community's approach from an integrated perspective.
    Source
    Journal of documentation. 62(2006) no.3, S.350-371
  5. Bock, H.-H.: Datenanalyse zur Strukturierung und Ordnung von Information (1989) 0.03 
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    Pages
    S.1-22
  6. Dubin, D.: Dimensions and discriminability (1998) 0.03 
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    Date
    22. 9.1997 19:16:05
    Pages
    S.39-44
  7. Jenkins, C.: Automatic classification of Web resources using Java and Dewey Decimal Classification (1998) 0.03 
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    Date
    1. 8.1996 22:08:06
    Source
    Computer networks and ISDN systems. 30(1998) nos.1/7, S.646-648
  8. Yoon, Y.; Lee, C.; Lee, G.G.: ¬An effective procedure for constructing a hierarchical text classification system (2006) 0.03 
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    Date
    22. 7.2006 16:24:52
    Source
    Journal of the American Society for Information Science and Technology. 57(2006) no.3, S.431-442
  9. Yi, K.: Automatic text classification using library classification schemes : trends, issues and challenges (2007) 0.03 
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    Date
    22. 9.2008 18:31:54
    Source
    International cataloguing and bibliographic control. 36(2007) no.4, S.78-82
  10. Liu, R.-L.: Context recognition for hierarchical text classification (2009) 0.02 
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    Date
    22. 3.2009 19:11:54
    Source
    Journal of the American Society for Information Science and Technology. 60(2009) no.4, S.803-813
  11. Pfeffer, M.: Automatische Vergabe von RVK-Notationen mittels fallbasiertem Schließen (2009) 0.02 
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    Date
    22. 8.2009 19:51:28
    Pages
    S.245-253
  12. Zhu, W.Z.; Allen, R.B.: Document clustering using the LSI subspace signature model (2013) 0.02 
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    Date
    23. 3.2013 13:22:36
    Source
    Journal of the American Society for Information Science and Technology. 64(2013) no.4, S.844-860
  13. Egbert, J.; Biber, D.; Davies, M.: Developing a bottom-up, user-based method of web register classification (2015) 0.02 
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    Date
    4. 8.2015 19:22:04
    Source
    Journal of the Association for Information Science and Technology. 66(2015) no.9, S.1817-1831
  14. Mengle, S.; Goharian, N.: Passage detection using text classification (2009) 0.02 
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    Date
    22. 3.2009 19:14:43
    Source
    Journal of the American Society for Information Science and Technology. 60(2009) no.4, S.814-825
  15. Liu, R.-L.: ¬A passage extractor for classification of disease aspect information (2013) 0.02 
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  16. Reiner, U.: Automatische DDC-Klassifizierung von bibliografischen Titeldatensätzen (2009) 0.02 
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  17. Khoo, C.S.G.; Ng, K.; Ou, S.: ¬An exploratory study of human clustering of Web pages (2003) 0.02 
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  18. Automatic classification research at OCLC (2002) 0.01 
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  20. Schiminovich, S.: Automatic classification and retrieval of documents by means of a bibliographic pattern discovery algorithm (1971) 0.00 
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    Information storage and retrieval. 6(1971), S.417-435

Authors

Years

Languages

  • e 157
  • d 41
  • chi 1
  • More… Less…

Types

  • a 177
  • el 14
  • x 9
  • m 4
  • r 4
  • s 2
  • d 1
  • More… Less…

Themes

Subjects