Combined Learning and Use for a Mixture Model Equivalent to the RBF Classifier

David J. Miller, Hasan S. Uyar

    Research output: Contribution to journalArticle

    25 Scopus citations

    Abstract

    We show that the decision function of a radial basis function (RBF) classifier is equivalent in form to the Bayes-optimal discriminant associated with a special kind of mixture-based statistical model. The relevant mixture model is a type of mixture-of-experts model for which class labels, like continuous-valued features, are assumed to have been generated randomly, conditional on the mixture component of origin. The new interpretation shows that RBF classifiers effectively assume a probability model, which, moreover, is easily determined given the designed RBF. This interpretation also suggests a statistical learning objective as an alternative to standard methods for designing the RBF-equivalent models. The statistical objective is especially useful for incorporating unlabeled data to enhance learning. Finally, it is observed that any new data to classify are simply additional unlabeled data. Thus, we suggest a combined learning and use paradigm, to be invoked whenever there are new data to classify.

    Original languageEnglish (US)
    Pages (from-to)281-293
    Number of pages13
    JournalNeural computation
    Volume10
    Issue number2
    DOIs
    StatePublished - Feb 15 1998

    All Science Journal Classification (ASJC) codes

    • Arts and Humanities (miscellaneous)
    • Cognitive Neuroscience

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