Early Stop Criterion from the Bootstrap Ensemble

Lars Kai Hansen, Jan Larsen, Torben L. Fog

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    Abstract

    This paper addresses the problem of generalization error estimation in neural networks. A new early stop criterion based on a Bootstrap estimate of the generalization error is suggested. The estimate does not require the network to be trained to the minimum of the cost function, as required by other methods based on asymptotic theory. Moreover, in contrast to methods based on cross-validation which require data left out for testing, and thus biasing the estimate, the Bootstrap technique does not have this disadvantage. The potential of the suggested technique is demonstrated on various time-series problems
    Original languageEnglish
    Title of host publicationProceedings of IEEE ICASSP'97
    Place of PublicationMunich, Germany
    PublisherIEEE
    Publication date1997
    Pages3205-3208
    ISBN (Print)0-8186-7919-0
    DOIs
    Publication statusPublished - 1997
    Event1997 IEEE International Conference on Acoustics, Speech, and Signal Processing - Munich, Germany
    Duration: 21 Apr 199724 Apr 1997
    https://ieeexplore.ieee.org/xpl/conhome/4635/proceeding

    Conference

    Conference1997 IEEE International Conference on Acoustics, Speech, and Signal Processing
    Country/TerritoryGermany
    CityMunich
    Period21/04/199724/04/1997
    Internet address

    Bibliographical note

    Copyright: 1997 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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