A pertinent issue in the development of automated classifiers is the combination of information extracted from the data with a priori information that we may have about the classes in questions. Incorporating prior knowledge is particularly useful if the data is incomplete or imprecise. Bayesian networks offer an ideal representation for the combination of a priori knowledge with data. This paper discusses the implementation of a Bayesian network classifier for the purpose of classifying underwater mines, using knowledge that is known by experts (sonar operators) about the distinguishing characteristics of mines, such as size, shape, shadow and resonance.


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    Title :

    Classification of sonar signals using Bayesian networks


    Contributors:


    Publication date :

    1998


    Size :

    4 Seiten, 5 Quellen




    Type of media :

    Conference paper


    Type of material :

    Print


    Language :

    English




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