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Distance Function Learning for Supervised Similarity Assessment

  • Chapter
Case-Based Reasoning on Images and Signals

Part of the book series: Studies in Computational Intelligence ((SCI,volume 73))

Summary

Assessing the similarity between cases is a prerequisite for many case-based reasoning tasks. This chapter centers on distance function learning for supervised similarity assessment. First a framework for supervised similarity assessment is introduced. Second, three supervised distance function learning approaches from the areas of pattern classification, supervised clustering, and information retrieval are discussed, and their results for two supervised learning tasks will be explained and visualized. In each of these different areas, we show how the method can be applied to areas of case-based reasoning. Finally, a detailed literature survey will be given.

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Author information

Authors and Affiliations

  1. Department of Computer Science, University of Houston, Houston, TX, 77204-3010, USA

    A. Bagherjeiran & C. F. Eick

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  1. A. Bagherjeiran
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  2. C. F. Eick
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Editors and Affiliations

  1. Institute of Computer Vision and Applied Computer Sciences, IBaI, Arno-Nitzsche-Str.43, 04277, Leipzig, Germany

    Petra Perner

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© 2008 Springer-Verlag Berlin Heidelberg

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Bagherjeiran, A., Eick, C.F. (2008). Distance Function Learning for Supervised Similarity Assessment. In: Perner, P. (eds) Case-Based Reasoning on Images and Signals. Studies in Computational Intelligence, vol 73. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73180-1_3

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  • DOI: https://doi.org/10.1007/978-3-540-73180-1_3

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-73178-8

  • Online ISBN: 978-3-540-73180-1

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