Search (9 results, page 1 of 1)

  • × theme_ss:"Case Based Reasoning"
  1. Chen, Z.: ¬A conceptual model for storage and retrieval of short scientific texts (1993) 0.03
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    Abstract
    A conceptual model for integrating short scientific texts is described, which extends classical text storage and retrieval. A brief comparison with related approaches (such as case-based reasoning and classification-based reasoning) is also provided
  2. Althoff, K.-D.; Wess, S.; Manago, M.; Bergmann, R.; Maurer, F.; Auriol, E.; Conruyt, N.; Traphöner, R.; Bräuer, M.; Dittrich, S.: Induction and case-based reasoning for classification tasks (1994) 0.01
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    Abstract
    We present 2 techniques for reasoning from cases to solve classification tasks: induction and case-based reasoning. We contrast the 2 technologies (that are often confused) and show how they complement each other. Based on this, we describe how they are integrated in one single platform for reasoning from cases: the INRECA system
    Series
    Studies in classification, data analysis, and knowledge organization
  3. Pfeffer, M.: Automatische Vergabe von RVK-Notationen mittels fallbasiertem Schließen (2009) 0.01
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    Date
    22. 8.2009 19:51:28
    Footnote
    Vgl. auch die Präsentationen unter: http://www.bibliothek.uni-regensburg.de/Systematik/pdf/Anw2008_PPT1.pdf. http://blog.bib.uni-mannheim.de/Classification/wp-content/uploads/2007/10/hu-berlin-2007-2.pdf. Volltexte unter:
  4. Rissland, E.L.; Daniels, J.J.: ¬The synergistic application of CBR to IR (1996) 0.00
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    Abstract
    Discusses a hybrid approach combining case-based reasoning and information retrieval for the retrieval of full text documents. It takes as input a standard symbolic representation of a problem case and retrieves text of relevant cases from a document collection dramatically larger than the case base available to the CBR system. It works by performing a standard HYPO style analysis and uses the texts associated with important classes of cases found in this analysis to seed a modified version of INQUERY's relevance feedback mechanism in order to generate a query composed of individual terms or pairs of terms. It exteds the reach of CBR to much larger corpora, and it anbales the injection of knowledge based techniques into traditional IR. Describes the CBR-IR approach and reports on-going experiments
  5. Golding, A.R.; Rosenbloom, P.S.: Improving accuracy by combining rule-based and case-based reasoning (1996) 0.00
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    Date
    6. 3.1997 16:22:15
  6. Ram, A.; Santamaria, J.C.: Continuous case-based reasoning (1997) 0.00
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    Date
    6. 3.1997 16:22:15
  7. Kohno, T.: Error repair and knowledge acquisition via case-based reasoning (1997) 0.00
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    Date
    6. 3.1997 16:22:15
  8. Mazzucchelli, A.; Sartori , F.: String similarity in CBR platforms : a preliminary study (2014) 0.00
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    Pages
    S.22-29
  9. Akerele, O.; David, A.; Osofisan, A.: Using the concepts of Case Based Reasoning and Basic Categories for enhancing adaptation to the user's level of knowledge in Decision Support System (2014) 0.00
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    Source
    Knowledge organization in the 21st century: between historical patterns and future prospects. Proceedings of the Thirteenth International ISKO Conference 19-22 May 2014, Kraków, Poland. Ed.: Wieslaw Babik