Search (94 results, page 1 of 5)

  • × theme_ss:"Automatisches Klassifizieren"
  1. Dubin, D.: Dimensions and discriminability (1998) 0.11 
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    Abstract
    Visualization interfaces can improve subject access by highlighting the inclusion of document representation components in similarity and discrimination relationships. Within a set of retrieved documents, what kinds of groupings can index terms and subject headings make explicit? The role of controlled vocabulary in classifying search output is examined
    Date
    22. 9.1997 19:16:05
    Imprint
    Urbana-Champaign, IL : Illinois University at Urbana-Champaign, Graduate School of Library and Information Science
    Source
    Visualizing subject access for 21st century information resources: Papers presented at the 1997 Clinic on Library Applications of Data Processing, 2-4 Mar 1997, Graduate School of Library and Information Science, University of Illinois at Urbana-Champaign. Ed.: P.A. Cochrane et al
  2. 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
  3. Huang, Y.-L.: ¬A theoretic and empirical research of cluster indexing for Mandarine Chinese full text document (1998) 0.05 
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    Abstract
    Since most popular commercialized systems for full text retrieval are designed with full text scaning and Boolean logic query mode, these systems use an oversimplified relationship between the indexing form and the content of document. Reports the use of Singular Value Decomposition (SVD) to develop a Cluster Indexing Model (CIM) based on a Vector Space Model (VSM) in orer to explore the index theory of cluster indexing for chinese full text documents. From a series of experiments, it was found that the indexing performance of CIM is better than traditional VSM, and has almost equivalent effectiveness of the authority control of index terms
    Source
    Bulletin of library and information science. 1998, no.24, S.44-68
  4. HaCohen-Kerner, Y. et al.: Classification using various machine learning methods and combinations of key-phrases and visual features (2016) 0.05 
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    Date
    1. 2.2016 18:25:22
    Series
    Lecture notes in computer science ; 9398
  5. Classification, automation, and new media : Proceedings of the 24th Annual Conference of the Gesellschaft für Klassifikation e.V., University of Passau, March 15 - 17, 2000 (2002) 0.04 
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    Abstract
    Given the huge amount of information in the internet and in practically every domain of knowledge that we are facing today, knowledge discovery calls for automation. The book deals with methods from classification and data analysis that respond effectively to this rapidly growing challenge. The interested reader will find new methodological insights as well as applications in economics, management science, finance, and marketing, and in pattern recognition, biology, health, and archaeology.
    Content
    Data Analysis, Statistics, and Classification.- Pattern Recognition and Automation.- Data Mining, Information Processing, and Automation.- New Media, Web Mining, and Automation.- Applications in Management Science, Finance, and Marketing.- Applications in Medicine, Biology, Archaeology, and Others.- Author Index.- Subject Index.
  6. Yoon, Y.; Lee, C.; Lee, G.G.: ¬An effective procedure for constructing a hierarchical text classification system (2006) 0.03 
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    Date
    22. 7.2006 16:24:52
    Source
    Journal of the American Society for Information Science and Technology. 57(2006) no.3, S.431-442
  7. Liu, R.-L.: Context recognition for hierarchical text classification (2009) 0.03 
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    Date
    22. 3.2009 19:11:54
    Source
    Journal of the American Society for Information Science and Technology. 60(2009) no.4, S.803-813
  8. Zhu, W.Z.; Allen, R.B.: Document clustering using the LSI subspace signature model (2013) 0.03 
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    Date
    23. 3.2013 13:22:36
    Source
    Journal of the American Society for Information Science and Technology. 64(2013) no.4, S.844-860
  9. Egbert, J.; Biber, D.; Davies, M.: Developing a bottom-up, user-based method of web register classification (2015) 0.03 
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    Date
    4. 8.2015 19:22:04
    Source
    Journal of the Association for Information Science and Technology. 66(2015) no.9, S.1817-1831
  10. Mengle, S.; Goharian, N.: Passage detection using text classification (2009) 0.02 
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    Date
    22. 3.2009 19:14:43
    Source
    Journal of the American Society for Information Science and Technology. 60(2009) no.4, S.814-825
  11. 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
    Source
    Journal of the American Society for Information Science and Technology. 64(2013) no.11, S.2265-2277
  12. Reiner, U.: Automatische DDC-Klassifizierung bibliografischer Titeldatensätze der Deutschen Nationalbibliografie (2009) 0.02 
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    Date
    22. 1.2010 14:41:24
    Footnote
    Vortrag gehalten am 03.06.2009 auf dem 98. Bibliothekartag 2009 in Erfurt; erscheint in: Dialog mit Biliotheken. Vgl. auch: http://www.gbv.de/vgm/info/biblio/01VZG/06Publikationen/2009/index.
  13. Borko, H.: Research in computer based classification systems (1985) 0.02 
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    Abstract
    The selection in this reader by R. M. Needham and K. Sparck Jones reports an early approach to automatic classification that was taken in England. The following selection reviews various approaches that were being pursued in the United States at about the same time. It then discusses a particular approach initiated in the early 1960s by Harold Borko, at that time Head of the Language Processing and Retrieval Research Staff at the System Development Corporation, Santa Monica, California and, since 1966, a member of the faculty at the Graduate School of Library and Information Science, University of California, Los Angeles. As was described earlier, there are two steps in automatic classification, the first being to identify pairs of terms that are similar by virtue of co-occurring as index terms in the same documents, and the second being to form equivalence classes of intersubstitutable terms. To compute similarities, Borko and his associates used a standard correlation formula; to derive classification categories, where Needham and Sparck Jones used clumping, the Borko team used the statistical technique of factor analysis. The fact that documents can be classified automatically, and in any number of ways, is worthy of passing notice. Worthy of serious attention would be a demonstra tion that a computer-based classification system was effective in the organization and retrieval of documents. One reason for the inclusion of the following selection in the reader is that it addresses the question of evaluation. To evaluate the effectiveness of their automatically derived classification, Borko and his team asked three questions. The first was Is the classification reliable? in other words, could the categories derived from one sample of texts be used to classify other texts? Reliability was assessed by a case-study comparison of the classes derived from three different samples of abstracts. The notso-surprising conclusion reached was that automatically derived classes were reliable only to the extent that the sample from which they were derived was representative of the total document collection. The second evaluation question asked whether the classification was reasonable, in the sense of adequately describing the content of the document collection. The answer was sought by comparing the automatically derived categories with categories in a related classification system that was manually constructed. Here the conclusion was that the automatic method yielded categories that fairly accurately reflected the major area of interest in the sample collection of texts; however, since there were only eleven such categories and they were quite broad, they could not be regarded as suitable for use in a university or any large general library. The third evaluation question asked whether automatic classification was accurate, in the sense of producing results similar to those obtainabie by human cIassifiers. When using human classification as a criterion, automatic classification was found to be 50 percent accurate.
  14. 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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  15. Lindholm, J.; Schönthal, T.; Jansson , K.: Experiences of harvesting Web resources in engineering using automatic classification (2003) 0.02 
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    Abstract
    Authors describe the background and the work involved in setting up Engine-e, a Web index that uses automatic classification as a mean for the selection of resources in Engineering. Considerations in offering a robot-generated Web index as a successor to a manually indexed quality-controlled subject gateway are also discussed
  16. Losee, R.M.; Haas, S.W.: Sublanguage terms : dictionaries, usage, and automatic classification (1995) 0.01 
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    Abstract
    The use of terms from natural and social science titles and abstracts is studied from the perspective of sublanguages and their specialized dictionaries. Explores different notions of sublanguage distinctiveness. Object methods for separating hard and soft sciences are suggested based on measures of sublanguage use, dictionary characteristics, and sublanguage distinctiveness. Abstracts were automatically classified with a high degree of accuracy by using a formula that condsiders the degree of uniqueness of terms in each sublanguage. This may prove useful for text filtering of information retrieval systems
    Source
    Journal of the American Society for Information Science. 46(1995) no.7, S.519-529
  17. Zhang, X: Rough set theory based automatic text categorization (2005) 0.01 
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    Abstract
    Der Forschungsbericht "Rough Set Theory Based Automatic Text Categorization and the Handling of Semantic Heterogeneity" von Xueying Zhang ist in Buchform auf Englisch erschienen. Zhang hat in ihrer Arbeit ein Verfahren basierend auf der Rough Set Theory entwickelt, das Beziehungen zwischen Schlagwörtern verschiedener Vokabulare herstellt. Sie war von 2003 bis 2005 Mitarbeiterin des IZ und ist seit Oktober 2005 Associate Professor an der Nanjing University of Science and Technology.
    Footnote
    Nanjing University of Science and Technology, Diss.
  18. Subramanian, S.; Shafer, K.E.: Clustering (2001) 0.01 
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    Date
    5. 5.2003 14:17:22
  19. Subramanian, S.; Shafer, K.E.: Clustering (1998) 0.01 
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    Abstract
    This article presents our exploration of computer science clustering algorithms as they relate to the Scorpion system. Scorpion is a research project at OCLC that explores the indexing and cataloging of electronic resources. For a more complete description of the Scorpion, please visit the Scorpion Web site at <http://purl.oclc.org/scorpion>
  20. Shafer, K.E.: Evaluating Scorpion results (1998) 0.01 
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    Abstract
    Scorpion is a research project at OCLC that builds tools for automatic subject assignment by combining library science and information retrieval techniques. A thesis of Scorpion is that the Dewey Decimal Classification (Dewey) can be used to perform automatic subject assignment for electronic items.

Authors

Years

Languages

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  • chi 1
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Types

  • a 84
  • el 8
  • m 3
  • s 1
  • x 1
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Subjects