Search (26 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) 0.07
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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. Chan, L.M.; Lin, X.; Zeng, M.L.: Structural and multilingual approaches to subject access on the Web (2000) 0.03
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  3. Subramanian, S.; Shafer, K.E.: Clustering (2001) 0.02
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    Date
    5. 5.2003 14:17:22
  4. Chung, Y.M.; Lee, J.Y.: ¬A corpus-based approach to comparative evaluation of statistical term association measures (2001) 0.02
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    Abstract
    Statistical association measures have been widely applied in information retrieval research, usually employing a clustering of documents or terms on the basis of their relationships. Applications of the association measures for term clustering include automatic thesaurus construction and query expansion. This research evaluates the similarity of six association measures by comparing the relationship and behavior they demonstrate in various analyses of a test corpus. Analysis techniques include comparisons of highly ranked term pairs and term clusters, analyses of the correlation among the association measures using Pearson's correlation coefficient and MDS mapping, and an analysis of the impact of a term frequency on the association values by means of z-score. The major findings of the study are as follows: First, the most similar association measures are mutual information and Yule's coefficient of colligation Y, whereas cosine and Jaccard coefficients, as well as X**2 statistic and likelihood ratio, demonstrate quite similar behavior for terms with high frequency. Second, among all the measures, the X**2 statistic is the least affected by the frequency of terms. Third, although cosine and Jaccard coefficients tend to emphasize high frequency terms, mutual information and Yule's Y seem to overestimate rare terms
  5. Hu, G.; Zhou, S.; Guan, J.; Hu, X.: Towards effective document clustering : a constrained K-means based approach (2008) 0.02
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  6. HaCohen-Kerner, Y. et al.: Classification using various machine learning methods and combinations of key-phrases and visual features (2016) 0.02
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    Date
    1. 2.2016 18:25:22
  7. Hagedorn, K.; Chapman, S.; Newman, D.: Enhancing search and browse using automated clustering of subject metadata (2007) 0.01
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    Source
    D-Lib magazine. 13(2007) nos.7/8, x S
  8. Choi, B.; Peng, X.: Dynamic and hierarchical classification of Web pages (2004) 0.01
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  9. Liu, X.; Yu, S.; Janssens, F.; Glänzel, W.; Moreau, Y.; Moor, B.de: Weighted hybrid clustering by combining text mining and bibliometrics on a large-scale journal database (2010) 0.01
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  10. Wu, M.; Liu, Y.-H.; Brownlee, R.; Zhang, X.: Evaluating utility and automatic classification of subject metadata from Research Data Australia (2021) 0.01
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  11. Peng, F.; Huang, X.: Machine learning for Asian language text classification (2007) 0.01
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  12. Pong, J.Y.-H.; Kwok, R.C.-W.; Lau, R.Y.-K.; Hao, J.-X.; Wong, P.C.-C.: ¬A comparative study of two automatic document classification methods in a library setting (2008) 0.01
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  13. Smiraglia, R.P.; Cai, X.: Tracking the evolution of clustering, machine learning, automatic indexing and automatic classification in knowledge organization (2017) 0.01
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  14. Bock, H.-H.: Datenanalyse zur Strukturierung und Ordnung von Information (1989) 0.01
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    Pages
    S.1-22
  15. Dubin, D.: Dimensions and discriminability (1998) 0.01
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    Date
    22. 9.1997 19:16:05
  16. 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
  17. 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
  18. 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
  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