In our recent paper we demonstrated that the standard Bayes classifier, when applied to a problem characterised by imprecise likelihood functions, produces results which are inconsistent with our intuition. As a more appropriate alternative to the standard Bayes classifier, we proposed in [1] a classification method based on the transferrable belief model (TBM). Mahler [2, ch. 4-8] recently proposed a novel approach to Bayesian estimation, fusion, and classification, applicable to situations where the information (priors, measurements, likelihoods) is imprecise and vague in addition to being random. The purpose of this letter is to demonstrate that Mahler's approach can produce identical results to those obtained using the TBM classifier.


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    Title :

    Target Classification with Imprecise Likelihoods: Mahler's Approach


    Contributors:
    Ristic, B (author)


    Publication date :

    2011-04-01


    Size :

    463799 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



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