Bayesian approaches to supervised learning use priors on the classifier parameters. However, few priors aim at achieving "sparse" classifiers, where irrelevant/redundant parameters are automatically set to zero. Two well-known ways of obtaining sparse classifiers are: use a zero-mean Laplacian prior on the parameters, and the "support vector machine" (SVM). Whether one uses a Laplacian prior or an SVM, one still needs to specify/estimate the parameters that control the degree of sparseness of the resulting classifiers. We propose a Bayesian approach to learning sparse classifiers which does not involve any parameters controlling the degree of sparseness. This is achieved by a hierarchical-Bayes interpretation of the Laplacian prior, followed by the adoption of a Jeffreys' non-informative hyper-prior Implementation is carried out by an EM algorithm. Experimental evaluation of the proposed method shows that it performs competitively with (often better than) the best classification techniques available.


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    Titel :

    Bayesian learning of sparse classifiers


    Beteiligte:
    Figueiredo, M.A.T. (Autor:in) / Jain, A.K. (Autor:in)


    Erscheinungsdatum :

    2001-01-01


    Format / Umfang :

    659465 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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