Search (44 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) 0.04 
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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) 0.02 
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    Date
    1. 8.1996 22:08:06
  3. Yoon, Y.; Lee, C.; Lee, G.G.: ¬An effective procedure for constructing a hierarchical text classification system (2006) 0.02 
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    Date
    22. 7.2006 16:24:52
  4. Liu, R.-L.: Context recognition for hierarchical text classification (2009) 0.02 
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    Date
    22. 3.2009 19:11:54
  5. Hung, C.-M.; Chien, L.-F.: Web-based text classification in the absence of manually labeled training documents (2007) 0.02 
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  6. Liu, R.-L.: ¬A passage extractor for classification of disease aspect information (2013) 0.02 
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    Date
    28.10.2013 19:22:57
  7. Subramanian, S.; Shafer, K.E.: Clustering (2001) 0.01 
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    Date
    5. 5.2003 14:17:22
  8. Reiner, U.: Automatische DDC-Klassifizierung von bibliografischen Titeldatensätzen (2009) 0.01 
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    Date
    22. 8.2009 12:54:24
  9. 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
  10. Miyamoto, S.: Information clustering based an fuzzy multisets (2003) 0.01 
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    Abstract
    A fuzzy multiset model for information clustering is proposed with application to information retrieval on the World Wide Web. Noting that a search engine retrieves multiple occurrences of the same subjects with possibly different degrees of relevance, we observe that fuzzy multisets provide an appropriate model of information retrieval on the WWW. Information clustering which means both term clustering and document clustering is considered. Three methods of the hard c-means, fuzzy c-means, and an agglomerative method using cluster centers are proposed. Two distances between fuzzy multisets and algorithms for calculating cluster centers are defined. Theoretical properties concerning the clustering algorithms are studied. Illustrative examples are given to show how the algorithms work.
  11. Huang, Y.-L.: ¬A theoretic and empirical research of cluster indexing for Mandarine Chinese full text document (1998) 0.01 
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  12. Fagni, T.; Sebastiani, F.: Selecting negative examples for hierarchical text classification: An experimental comparison (2010) 0.01 
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    Abstract
    Hierarchical text classification (HTC) approaches have recently attracted a lot of interest on the part of researchers in human language technology and machine learning, since they have been shown to bring about equal, if not better, classification accuracy with respect to their "flat" counterparts while allowing exponential time savings at both learning and classification time. A typical component of HTC methods is a "local" policy for selecting negative examples: Given a category c, its negative training examples are by default identified with the training examples that are negative for c and positive for the categories which are siblings of c in the hierarchy. However, this policy has always been taken for granted and never been subjected to careful scrutiny since first proposed 15 years ago. This article proposes a thorough experimental comparison between this policy and three other policies for the selection of negative examples in HTC contexts, one of which (BEST LOCAL (k)) is being proposed for the first time in this article. We compare these policies on the hierarchical versions of three supervised learning algorithms (boosting, support vector machines, and naïve Bayes) by performing experiments on two standard TC datasets, REUTERS-21578 and RCV1-V2.
  13. Godby, C. J.; Stuler, J.: ¬The Library of Congress Classification as a knowledge base for automatic subject categorization (2001) 0.01 
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  14. Pfister, J.: Clustering von Patent-Dokumenten am Beispiel der Datenbanken des Fachinformationszentrums Karlsruhe (2006) 0.01 
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    Source
    Effektive Information Retrieval Verfahren in Theorie und Praxis: ausgewählte und erweiterte Beiträge des Vierten Hildesheimer Evaluierungs- und Retrievalworkshop (HIER 2005), Hildesheim, 20.7.2005. Hrsg.: T. Mandl u. C. Womser-Hacker
  15. Koch, T.; Ardö, A.; Noodén, L.: ¬The construction of a robot-generated subject index : DESIRE II D3.6a, Working Paper 1 (1999) 0.01 
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  16. Liu, R.-L.: Dynamic category profiling for text filtering and classification (2007) 0.01 
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  17. Denoyer, L.; Gallinari, P.: Bayesian network model for semi-structured document classification (2004) 0.01 
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  18. Liu, R.-L.: Context-based term frequency assessment for text classification (2010) 0.01 
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  19. Bock, H.-H.: Datenanalyse zur Strukturierung und Ordnung von Information (1989) 0.01 
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    Pages
    S.1-22
  20. Dubin, D.: Dimensions and discriminability (1998) 0.01 
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    Date
    22. 9.1997 19:16:05

Authors

Years

Languages

  • e 36
  • d 7
  • chi 1
  • More… Less…

Types

  • a 36
  • el 7
  • m 1
  • s 1
  • x 1
  • More… Less…

Themes