The probabilistic multi-hypothesis tracking (PMHT) algorithm is extended for application to classification. The PMHT model is reformulated as a bank of continuous-state hidden Markov models, allowing for supervised learning of the class-conditional probability density models, and for likelihood evaluation of multicomponent signals.


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    Title :

    Multicomponent signal classification using the PMHT algorithm


    Contributors:


    Publication date :

    2002-01-01


    Size :

    523525 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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