A generalization error estimate for nonlinear systems

Jan Larsen

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    Abstract

    A new estimate (GEN) of the generalization error is presented. The estimator is valid for both incomplete and nonlinear models. An incomplete model is characterized in that it does not model the actual nonlinear relationship perfectly. The GEN estimator has been evaluated by simulating incomplete models of linear and simple neural network systems. Within the linear system GEN is compared to the final prediction error criterion and the leave-one-out cross-validation technique. It was found that the GEN estimate of the true generalization error is less biased on the average. It is concluded that GEN is an applicable alternative in estimating the generalization at the expense of an increased complexity
    Original languageEnglish
    Title of host publicationProceedings of the IEEE-SP Workshop Neural Networks for Signal Processing
    PublisherIEEE
    Publication date1992
    Pages29-38
    ISBN (Print)07-80-30557-4
    DOIs
    Publication statusPublished - 1992
    Event1992 IEEE-SP Workshop of Neural Networks for Signal Processing - Helsingør, Denmark
    Duration: 31 Aug 19922 Sep 1992
    http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=631

    Workshop

    Workshop1992 IEEE-SP Workshop of Neural Networks for Signal Processing
    CountryDenmark
    CityHelsingør
    Period31/08/199202/09/1992
    Internet address

    Bibliographical note

    Copyright: 1992 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE

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