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Nonstandard Concepts of Similarity in Case-Based Reasoning

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Information Systems and Data Analysis

Summary

The present paper is motivated by the author’s work in learning, by his work on case-based learning and by his machine learning work in the case-based reasoning research project FABEL, in particular. The presentation is focussed on case-based classification, but the paper is more specific in one respect and more general in another. The core of the paper is more specific, as it is driven by investigations in learning, and it is more general, as the nonstandard concepts developed are of importance for case-based reasoning, in general. It seems that a much too restricted view at similarity is one of the basic drawbacks of recent research in case-based reasoning. A rigorous criticism is intended to set the stage for new approaches to similarity which may contribute to a considerable progress in the similarity-based use of cases. New generation approaches to similarity should allow both non-symmetric similarity concepts and similarity of more than two objects. Those concepts exist already in certain specific versions mainly in the cognitive sciences area. The present paper is introducing nonstandard concepts of similarity in a quite formal setting which allows an integration of well-known procedures like unification algorithms.

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Author information

Authors and Affiliations

  1. HTWK Leipzig (FH), FB IMN, Postfach 66, O-7030, Leipzig, Germany

    Klaus P. Jantke

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  1. Klaus P. Jantke
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Editor information

Editors and Affiliations

  1. Institut für Statistik, Rheinisch-Westfälische Technische Hochschule Aachen (RWTH), Wüllnerstr. 3, D-52056, Aachen, Germany

    Hans-Hermann Bock

  2. Forschungsstelle “Mathematische Logik” der Heidelberger Akademie der Wissenschaften Fachbereich Informatik, Universität Kaiserslautern, Erwin-Schrödinger-Str. 57, D-67653, Kaiserslautern, Germany

    Wolfgang Lenski

  3. Fachbereich Informatik, Universität Kaiserslautern, Erwin-Schrödinger-Str. 57, D-67653, Kaiserslautern, Germany

    Michael M. Richter

  4. Deutsches Forschungszentrum für Künstliche Intelligenz GmbH (DFKI), Erwin-Schrödinger-Str. 57, D-67653, Kaiserslautern, Germany

    Michael M. Richter

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© 1994 Springer-Verlag Berlin · Heidelberg

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Jantke, K.P. (1994). Nonstandard Concepts of Similarity in Case-Based Reasoning. In: Bock, HH., Lenski, W., Richter, M.M. (eds) Information Systems and Data Analysis. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-46808-7_3

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  • DOI: https://doi.org/10.1007/978-3-642-46808-7_3

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-58057-7

  • Online ISBN: 978-3-642-46808-7

  • eBook Packages: Springer Book Archive

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