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  • Selecting Parameter Values for Mahalanobis Distance Fuzzy Classifiers

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    18023.pdf (434.2Kb)
    Author(s)
    Deer, Peter
    Griffith University Author(s)
    Deer, Peter
    Eklund, Peter
    Year published
    2001
    Metadata
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    Abstract
    The fuzzy c-means clustering algorithm, and a related supervised classifier, require the a priori selection of a weighting parameter called the fuzzy exponent. This paper investigates suitable values of this fuzzy exponent using the criterion that fuzzy set memberships reflect class proportions in the mixed pixels of a remotely sensed imageThe fuzzy c-means clustering algorithm, and a related supervised classifier, require the a priori selection of a weighting parameter called the fuzzy exponent. This paper investigates suitable values of this fuzzy exponent using the criterion that fuzzy set memberships reflect class proportions in the mixed pixels of a remotely sensed image
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    Conference Title
    Proceedings of the 10th IEEE International Conference on Fuzzy Systems
    Publisher URI
    http://www.conferences.unimelb.edu.au/fuzzy/
    DOI
    https://doi.org/10.1109/FUZZ.2001.1009011
    Copyright Statement
    © 2001 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
    Publication URI
    http://hdl.handle.net/10072/9217
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    • Conference outputs

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