Combined learning and use for classification and regression models

David Jonathan Miller, Hasan S. Uyar, Lian Yah

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    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 do effectively assume a probability model which, moreover, is easily determined given the designed RBF. This interpretation also suggests a maximum likelihood learning objective, as an alternative to standard methods, for designing the RBF-equivalent models. This statistical objective is especially useful for incorporating unlabelled data within learning to enhance performance. While this approach might appear to be limited to applications involving a large, label-deficient training set, the scope of application is significantly extended with the observation that any new data to classify is also unlabelled data, available for learning. Thus, we suggest a combined learning and use paradigm, to be invoked whenever there is new data to classify. This new approach is tested for vowel recognition, given a small archive of examples from different speakers. For this problem, a conventional method is of necessity speaker-independent. By contrast, combined learning and use allows speaker-dependent adaptation, with resulting gains in performance.

    Original languageEnglish (US)
    Title of host publicationNeural Networks for Signal Processing - Proceedings of the IEEE Workshop
    PublisherIEEE
    Pages102-111
    Number of pages10
    StatePublished - 1997
    EventProceedings of the 1997 7th IEEE Workshop on Neural Networks for Signal Processing, NNSP'97 - Amelia Island, FL, USA
    Duration: Sep 24 1997Sep 26 1997

    Other

    OtherProceedings of the 1997 7th IEEE Workshop on Neural Networks for Signal Processing, NNSP'97
    CityAmelia Island, FL, USA
    Period9/24/979/26/97

    All Science Journal Classification (ASJC) codes

    • Signal Processing
    • Software
    • Electrical and Electronic Engineering

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