Search (29 results, page 1 of 2)

  • × type_ss:"a"
  • × theme_ss:"Automatisches Klassifizieren"
  1. Hotho, A.; Bloehdorn, S.: Data Mining 2004 : Text classification by boosting weak learners based on terms and concepts (2004) 0.12 
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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. Jenkins, C.: Automatic classification of Web resources using Java and Dewey Decimal Classification (1998) 0.04 
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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
  3. Greiner, G.: Intellektuelles und automatisches Klassifizieren (1981) 0.02 
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    VdDB/VDB-Rundschreiben. Sonderheft 71. Deutscher Bibliothekartag Regensburg, 9.-13.6.1981. S.3-7
  4. Kleinoeder, H.H.; Puzicha, J.: Automatische Katalogisierung am Beispiel einer Pilotanwendung (2002) 0.02 
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    Source
    Info 7. 17(2002) H.1, S.19-21
  5. Subramanian, S.; Shafer, K.E.: Clustering (2001) 0.02 
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    Date
    5. 5.2003 14:17:22
  6. Ardö, A.; Koch, T.: Automatic classification applied to full-text Internet documents in a robot-generated subject index (1999) 0.02 
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    Source
    Online information 99: 23rd International Online Information Meeting, Proceedings, London, 7-9 December 1999. Ed.: D. Raitt et al
  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. Möller, G.: Automatic classification of the World Wide Web using Universal Decimal Classification (1999) 0.01 
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    Online information 99: 23rd International Online Information Meeting, Proceedings, London, 7-9 December 1999. Ed.: D. Raitt et al
  9. Losee, R.M.; Haas, S.W.: Sublanguage terms : dictionaries, usage, and automatic classification (1995) 0.01 
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    Source
    Journal of the American Society for Information Science. 46(1995) no.7, S.519-529
  10. Panyr, J.: Automatische Indexierung und Klassifikation (1983) 0.01 
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    Automatisierung in der Klassifikation. Proc. 7. Jahrestagung der Gesellschaft für Klassifikation (Teil 1), Königswinter, 5.-8.4.1983. Hrsg.: I. Dahlberg u.a
  11. Fuhr, N.: Klassifikationsverfahren bei der automatischen Indexierung (1983) 0.01 
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    Automatisierung in der Klassifikation. Proc. 7. Jahrestagung der Gesellschaft für Klassifikation (Teil 1), Königswinter, 5.-8.4.1983. Hrsg.: I. Dahlberg u.a
  12. Krauth, J.: Evaluation von Verfahren der automatischen Klassifikation (1983) 0.01 
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    Automatisierung in der Klassifikation. Proc. 7. Jahrestagung der Gesellschaft für Klassifikation (Teil 1), Königswinter, 5.-8.4.1983. Hrsg.: I. Dahlberg u.a
  13. Bock, H.-H.: Datenanalyse zur Strukturierung und Ordnung von Information (1989) 0.01 
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    Pages
    S.1-22
  14. Dubin, D.: Dimensions and discriminability (1998) 0.01 
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    Date
    22. 9.1997 19:16:05
  15. 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
  16. 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
  17. Ma, Z.; Sun, A.; Cong, G.: On predicting the popularity of newly emerging hashtags in Twitter (2013) 0.01 
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    Abstract
    Because of Twitter's popularity and the viral nature of information dissemination on Twitter, predicting which Twitter topics will become popular in the near future becomes a task of considerable economic importance. Many Twitter topics are annotated by hashtags. In this article, we propose methods to predict the popularity of new hashtags on Twitter by formulating the problem as a classification task. We use five standard classification models (i.e., Naïve bayes, k-nearest neighbors, decision trees, support vector machines, and logistic regression) for prediction. The main challenge is the identification of effective features for describing new hashtags. We extract 7 content features from a hashtag string and the collection of tweets containing the hashtag and 11 contextual features from the social graph formed by users who have adopted the hashtag. We conducted experiments on a Twitter data set consisting of 31 million tweets from 2 million Singapore-based users. The experimental results show that the standard classifiers using the extracted features significantly outperform the baseline methods that do not use these features. Among the five classifiers, the logistic regression model performs the best in terms of the Micro-F1 measure. We also observe that contextual features are more effective than content features.
    Source
    Journal of the American Society for Information Science and Technology. 64(2013) no.7, S.1399-1410
  18. Liu, R.-L.: Context recognition for hierarchical text classification (2009) 0.01 
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    Date
    22. 3.2009 19:11:54
  19. Pfeffer, M.: Automatische Vergabe von RVK-Notationen mittels fallbasiertem Schließen (2009) 0.01 
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    Date
    22. 8.2009 19:51:28
  20. 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

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