Search (47 results, page 2 of 3)

  • × theme_ss:"Automatisches Klassifizieren"
  • × year_i:[2000 TO 2010}
  1. Oberhauser, O.: Automatisches Klassifizieren und Bibliothekskataloge (2005) 0.00 
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    Abstract
    In der bibliothekarischen Welt sind die Vorzüge einer klassifikatorischen Inhaltserschließung seit jeher wohlbekannt. Auch im Zeitalter der Online-Kataloge gibt es dafür keinen wirklichen Ersatz, da - kurz formuliert - ein stichwortbasiertes Retrieval alleine mit Problemen wie Ambiguität und Mehrsprachigkeit nicht fertig zu werden vermag. Zahlreiche Online-Kataloge weisen daher Notationen verschiedener Klassifikationssysteme auf; allerdings sind die darauf basierenden Abfragemöglichkeiten meist noch arg unterentwickelt. Viele Datensätze in OPACs sind aber überhaupt nicht sachlich erschlossen, sei es, dass sie aus retrospektiv konvertierten Nominalkatalogen stammen, sei es, dass ein Mangel an personellen Ressourcen ihre inhaltliche Erschließung verhindert hat. Angesichts großer Mengen solcher Datensätze liegt ein Interesse an automatischen Verfahren zur Sacherschließung durchaus nahe.
  2. Lindholm, J.; Schönthal, T.; Jansson , K.: Experiences of harvesting Web resources in engineering using automatic classification (2003) 0.00 
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    Footnote
    Auch unter: http://www.ariadne.ac.uk/issue37/lindholm/ und http://engine-e.lub.lu.se/
  3. Ruiz, M.E.; Srinivasan, P.: Combining machine learning and hierarchical indexing structures for text categorization (2001) 0.00 
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    Date
    11. 5.2003 18:29:44
  4. Reiner, U.: Automatic analysis of DDC notations (2007) 0.00 
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    Content
    Vortrag während der EDUG-Konferenz der European DDC users' group am 11.06.2007 in Bern.
  5. Subramanian, S.; Shafer, K.E.: Clustering (2001) 0.00 
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    Date
    5. 5.2003 14:17:22
  6. Cathey, R.J.; Jensen, E.C.; Beitzel, S.M.; Frieder, O.; Grossman, D.: Exploiting parallelism to support scalable hierarchical clustering (2007) 0.00 
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    Abstract
    A distributed memory parallel version of the group average hierarchical agglomerative clustering algorithm is proposed to enable scaling the document clustering problem to large collections. Using standard message passing operations reduces interprocess communication while maintaining efficient load balancing. In a series of experiments using a subset of a standard Text REtrieval Conference (TREC) test collection, our parallel hierarchical clustering algorithm is shown to be scalable in terms of processors efficiently used and the collection size. Results show that our algorithm performs close to the expected O(n**2/p) time on p processors rather than the worst-case O(n**3/p) time. Furthermore, the O(n**2/p) memory complexity per node allows larger collections to be clustered as the number of nodes increases. While partitioning algorithms such as k-means are trivially parallelizable, our results confirm those of other studies which showed that hierarchical algorithms produce significantly tighter clusters in the document clustering task. Finally, we show how our parallel hierarchical agglomerative clustering algorithm can be used as the clustering subroutine for a parallel version of the buckshot algorithm to cluster the complete TREC collection at near theoretical runtime expectations.
  7. Automatic classification research at OCLC (2002) 0.00 
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    Date
    5. 5.2003 9:22:09
  8. Yoon, Y.; Lee, C.; Lee, G.G.: ¬An effective procedure for constructing a hierarchical text classification system (2006) 0.00 
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    Date
    22. 7.2006 16:24:52
  9. Yi, K.: Automatic text classification using library classification schemes : trends, issues and challenges (2007) 0.00 
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    Date
    22. 9.2008 18:31:54
  10. Yi, K.: Challenges in automated classification using library classification schemes (2006) 0.00 
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    Content
    Vortrag anlässlich der 72ND IFLA General Conference and Council, 20-24 August 2006, Seoul, Korea
  11. Drori, O.; Alon, N.: Using document classification for displaying search results (2003) 0.00 
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    Source
    Journal of information science. 29(2003) no.2, S.97-106
  12. Chung, Y.-M.; Noh, Y.-H.: Developing a specialized directory system by automatically classifying Web documents (2003) 0.00 
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    Source
    Journal of information science. 29(2003) no.2, S.117-126
  13. Li, T.; Zhu, S.; Ogihara, M.: Hierarchical document classification using automatically generated hierarchy (2007) 0.00 
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    Source
    Journal of intelligent information systems. 29(2007) no.2, S.211-230
  14. Liu, R.-L.: Context recognition for hierarchical text classification (2009) 0.00 
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    Date
    22. 3.2009 19:11:54
  15. Ribeiro-Neto, B.; Laender, A.H.F.; Lima, L.R.S. de: ¬An experimental study in automatically categorizing medical documents (2001) 0.00 
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    Date
    29. 9.2001 13:59:42
  16. Chung, Y.M.; Lee, J.Y.: ¬A corpus-based approach to comparative evaluation of statistical term association measures (2001) 0.00 
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    Date
    29. 9.2001 14:01:18
  17. Ibekwe-SanJuan, F.; SanJuan, E.: From term variants to research topics (2002) 0.00 
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    Source
    Knowledge organization. 29(2002) nos.3/4, S.181-197
  18. Giorgetti, D.; Sebastiani, F.: Automating survey coding by multiclass text categorization techniques (2003) 0.00 
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    Date
    9. 7.2006 10:29:12
  19. Kwon, O.W.; Lee, J.H.: Text categorization based on k-nearest neighbor approach for web site classification (2003) 0.00 
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    Date
    27.12.2007 17:32:29
  20. Mengle, S.; Goharian, N.: Passage detection using text classification (2009) 0.00 
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    Date
    22. 3.2009 19:14:43

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