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Introspective Learning to Build Case-Based Reasoning (CBR) Knowledge Containers

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Machine Learning and Data Mining in Pattern Recognition (MLDM 2003)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2734))

Abstract

Case Based Reasoning systems rely on competent case knowledge for effective problem-solving. However, for many problem solving tasks, notably design, simple retrieval from the case-base in not sufficient. Further knowledge is required to help effective retrieval and to undertake adaptation of the retrieved solution to suit the new problem better. This paper proposes methods to learn knowledge for the retrieval and adaptation knowledge containers exploiting the knowledge already captured in the case knowledge.

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References

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Author information

Authors and Affiliations

  1. School of Computing, The Robert Gordon University, Aberdeen, Scotland, UK

    Susan Craw

Authors
  1. Susan Craw
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Editor information

Editors and Affiliations

  1. Institute of Computer Vision and Applied Computer Sciences, Arndtstr. 4, 04275, Leipzig, Germany

    Petra Perner

  2. Center for Automation Research, University of Maryland, College Park, Maryland, 20742-3275, USA

    Azriel Rosenfeld

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© 2003 Springer-Verlag Berlin Heidelberg

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Craw, S. (2003). Introspective Learning to Build Case-Based Reasoning (CBR) Knowledge Containers. In: Perner, P., Rosenfeld, A. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2003. Lecture Notes in Computer Science, vol 2734. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45065-3_1

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  • DOI: https://doi.org/10.1007/3-540-45065-3_1

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-40504-7

  • Online ISBN: 978-3-540-45065-8

  • eBook Packages: Springer Book Archive

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