A theoretical framework is presented for target classification based on the belief theory on the continuous space. The proposed approach is applicable when class-conditioned densities of feature/attribute measurements are known only partially, as subjective models of a potential "betting" behaviour. Prior class probabilities may also be unknown. Numerical examples are provided to illustrate how the proposed approach is more cautious in decision making and produces very different results from those obtained using the Bayesian classifier.


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    Titel :

    Target classification approach based on the belief function theory


    Beteiligte:
    Ristic, B. (Autor:in) / Smets, P. (Autor:in)


    Erscheinungsdatum :

    2005-04-01


    Format / Umfang :

    1407245 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch





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