Search (56 results, page 1 of 3)

  • × theme_ss:"Automatisches Klassifizieren"
  1. Hotho, A.; Bloehdorn, S.: Data Mining 2004 : Text classification by boosting weak learners based on terms and concepts (2004) 0.09 
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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. Reiner, U.: Automatische DDC-Klassifizierung von bibliografischen Titeldatensätzen (2009) 0.04 
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    Date
    22. 8.2009 12:54:24
  3. Bock, H.-H.: Datenanalyse zur Strukturierung und Ordnung von Information (1989) 0.02 
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    Pages
    S.1-22
    Source
    Klassifikation und Ordnung. Tagungsband 12. Jahrestagung der Gesellschaft für Klassifikation, Darmstadt 17.-19.3.1988. Hrsg.: R. Wille
  4. Mengle, S.; Goharian, N.: Passage detection using text classification (2009) 0.02 
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    Abstract
    Passages can be hidden within a text to circumvent their disallowed transfer. Such release of compartmentalized information is of concern to all corporate and governmental organizations. Passage retrieval is well studied; we posit, however, that passage detection is not. Passage retrieval is the determination of the degree of relevance of blocks of text, namely passages, comprising a document. Rather than determining the relevance of a document in its entirety, passage retrieval determines the relevance of the individual passages. As such, modified traditional information-retrieval techniques compare terms found in user queries with the individual passages to determine a similarity score for passages of interest. In passage detection, passages are classified into predetermined categories. More often than not, passage detection techniques are deployed to detect hidden paragraphs in documents. That is, to hide information, documents are injected with hidden text into passages. Rather than matching query terms against passages to determine their relevance, using text-mining techniques, the passages are classified. Those documents with hidden passages are defined as infected. Thus, simply stated, passage retrieval is the search for passages relevant to a user query, while passage detection is the classification of passages. That is, in passage detection, passages are labeled with one or more categories from a set of predetermined categories. We present a keyword-based dynamic passage approach (KDP) and demonstrate that KDP outperforms statistically significantly (99% confidence) the other document-splitting approaches by 12% to 18% in the passage detection and passage category-prediction tasks. Furthermore, we evaluate the effects of the feature selection, passage length, ambiguous passages, and finally training-data category distribution on passage-detection accuracy.
    Date
    22. 3.2009 19:14:43
  5. 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
  6. Subramanian, S.; Shafer, K.E.: Clustering (2001) 0.01 
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    Date
    5. 5.2003 14:17:22
  7. HaCohen-Kerner, Y. et al.: Classification using various machine learning methods and combinations of key-phrases and visual features (2016) 0.01 
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    Date
    1. 2.2016 18:25:22
  8. Panyr, J.: Automatische Klassifikation und Information Retrieval : Anwendung und Entwicklung komplexer Verfahren in Information-Retrieval-Systemen und ihre Evaluierung (1986) 0.01 
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    Series
    Sprache und Information; Bd.12
  9. Yu, W.; Gong, Y.: Document clustering by concept factorization (2004) 0.01 
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    Date
    12. 2.2005 14:27:19
  10. Illing, S.: Automatisiertes klinisches Codieren (2021) 0.01 
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    Date
    27.11.2013 12:50:12
  11. Dubin, D.: Dimensions and discriminability (1998) 0.01 
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    Date
    22. 9.1997 19:16:05
  12. Automatic classification research at OCLC (2002) 0.01 
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    Date
    5. 5.2003 9:22:09
  13. 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
  14. 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
  15. 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
  16. Subramanian, S.; Shafer, K.E.: Clustering (1998) 0.01 
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    Date
    26.12.2011 12:38:19
  17. Shafer, K.E.: Evaluating Scorpion results (1998) 0.01 
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    Date
    26.12.2011 12:43:02
  18. Shen, D.; Chen, Z.; Yang, Q.; Zeng, H.J.; Zhang, B.; Lu, Y.; Ma, W.Y.: Web page classification through summarization (2004) 0.01 
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    Date
    12. 2.2005 14:27:19
  19. Liu, R.-L.: Context recognition for hierarchical text classification (2009) 0.01 
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    Date
    22. 3.2009 19:11:54
  20. Pfeffer, M.: Automatische Vergabe von RVK-Notationen mittels fallbasiertem Schließen (2009) 0.01 
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    Date
    22. 8.2009 19:51:28

Authors

Years

Languages

  • e 45
  • d 11

Types

  • a 45
  • el 11
  • d 1
  • m 1
  • r 1
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Themes