Search (53 results, page 1 of 3)

  • × theme_ss:"Automatisches Klassifizieren"
  1. Sojka, P.; Lee, M.; Rehurek, R.; Hatlapatka, R.; Kucbel, M.; Bouche, T.; Goutorbe, C.; Anghelache, R.; Wojciechowski, K.: Toolset for entity and semantic associations : Final Release (2013) 0.02
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    Field
    Mathematik
    Object
    Zentralblatt für Mathematik
  2. Fuhr, N.: Klassifikationsverfahren bei der automatischen Indexierung (1983) 0.02
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    Abstract
    Nach einer kurzen Einführung in die Darmstädter Projekte WAI und AIR werden die folgenden Themen behandelt: Ein Ansatz zur automatischen Klassifikation. Statistische Relationen für die Klassifikation. Indexieren von Dokumenten als Spezialfall der automatischen Klassifikation. Klassifikation von Elementen der Relevanzbeschreibung. Klassifikation zur Verbesserung der Relevanzbeschreibungen. Automatische Dokumentklassifikation und Automatische Indexierung klassifizierter Dokumente. Das Projekt AIR wird in Zusammenarbeit mit der Datenbasis INKA-PHYS des Fachinformationszentrums Energie, Physik, Mathematik in Karlsruhe durchgeführt
  3. Barthel, S.; Tönnies, S.; Balke, W.-T.: Large-scale experiments for mathematical document classification (2013) 0.02
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    Field
    Mathematik
    Object
    Zentralblatt für Mathematik
  4. Subramanian, S.; Shafer, K.E.: Clustering (2001) 0.01
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    Date
    11. 2.1997 20:11:23
    5. 5.2003 14:17:22
  5. Wille, J.: Automatisches Klassifizieren bibliographischer Beschreibungsdaten : Vorgehensweise und Ergebnisse (2006) 0.01
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    Abstract
    Diese Arbeit befasst sich mit den praktischen Aspekten des Automatischen Klassifizierens bibliographischer Referenzdaten. Im Vordergrund steht die konkrete Vorgehensweise anhand des eigens zu diesem Zweck entwickelten Open Source-Programms COBRA "Classification Of Bibliographic Records, Automatic". Es werden die Rahmenbedingungen und Parameter f¨ur einen Einsatz im bibliothekarischen Umfeld geklärt. Schließlich erfolgt eine Auswertung von Klassifizierungsergebnissen am Beispiel sozialwissenschaftlicher Daten aus der Datenbank SOLIS.
    Date
    10. 9.2006 19:23:31
  6. Shafer, K.E.: Automatic Subject Assignment via the Scorpion System (2001) 0.01
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    Date
    11. 2.1997 20:11:23
  7. Pfeffer, M.: Automatische Vergabe von RVK-Notationen mittels fallbasiertem Schließen (2009) 0.01
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    Date
    22. 8.2009 19:51:28
    23. 8.2009 9:46:44
  8. 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
  9. Subramanian, S.; Shafer, K.E.: Clustering (1998) 0.01
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    Date
    11. 2.1997 20:11:23
  10. Shafer, K.E.: Evaluating Scorpion results (1998) 0.01
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    Date
    11. 2.1997 20:11:23
  11. Shafer, K.E.: Evaluating Scorpion Results (2001) 0.01
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    Date
    11. 2.1997 20:11:23
  12. Montesi, M.; Navarrete, T.: Classifying web genres in context : A case study documenting the web genres used by a software engineer (2008) 0.01
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    Abstract
    This case study analyzes the Internet-based resources that a software engineer uses in his daily work. Methodologically, we studied the web browser history of the participant, classifying all the web pages he had seen over a period of 12 days into web genres. We interviewed him before and after the analysis of the web browser history. In the first interview, he spoke about his general information behavior; in the second, he commented on each web genre, explaining why and how he used them. As a result, three approaches allow us to describe the set of 23 web genres obtained: (a) the purposes they serve for the participant; (b) the role they play in the various work and search phases; (c) and the way they are used in combination with each other. Further observations concern the way the participant assesses quality of web-based resources, and his information behavior as a software engineer.
    Date
    1. 8.2008 12:17:23
  13. Illing, S.: Automatisiertes klinisches Codieren (2021) 0.01
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    Date
    10.11.2021 19:11:23
  14. Savic, D.: Automatic classification of office documents : review of available methods and techniques (1995) 0.00
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    Date
    23. 7.1996 10:28:09
  15. Hotho, A.; Bloehdorn, S.: Data Mining 2004 : Text classification by boosting weak learners based on terms and concepts (2004) 0.00
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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
  16. 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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    Date
    17. 8.2003 14:17:23
  17. Sun, A.; Lim, E.-P.; Ng, W.-K.: Performance measurement framework for hierarchical text classification (2003) 0.00
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    Date
    20. 8.2003 20:23:39
  18. Gauch, S.; Chandramouli, A.; Ranganathan, S.: Training a hierarchical classifier using inter document relationships (2009) 0.00
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    Date
    23. 2.2009 18:34:38
  19. Ko, Y.: ¬A new term-weighting scheme for text classification using the odds of positive and negative class probabilities (2015) 0.00
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    Date
    24.11.2015 14:23:26
  20. Teich, E.; Degaetano-Ortlieb, S.; Fankhauser, P.; Kermes, H.; Lapshinova-Koltunski, E.: ¬The linguistic construal of disciplinarity : a data-mining approach using register features (2016) 0.00
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    Date
    12. 6.2016 20:23:08

Years

Languages

  • e 43
  • d 10

Types

  • a 43
  • el 9
  • m 1
  • r 1
  • x 1
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