Adaptive Regularization of Neural Networks Using Conjugate Gradient

Cyril Goutte, Jan Larsen

    Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

    369 Downloads (Pure)

    Abstract

    Andersen et al. (1997) and Larsen et al. (1996, 1997) suggested a regularization scheme which iteratively adapts regularization parameters by minimizing validation error using simple gradient descent. In this contribution we present an improved algorithm based on the conjugate gradient technique. Numerical experiments with feedforward neural networks successfully demonstrate improved generalization ability and lower computational cost
    Original languageEnglish
    Title of host publicationAcoustics, Speech and Signal Processing, 1998. Proceedings of the 1998 IEEE International Conference on
    Volume2
    Place of PublicationNew York
    PublisherIEEE
    Publication date1998
    Pages1201-1204
    ISBN (Print)0-7803-4428-6
    DOIs
    Publication statusPublished - 1998
    EventICASSP´98, IEEE 1998 Int.Conf. on Acoustics, Speech, and Signal Processing - Seattle, USA
    Duration: 1 Jan 1998 → …

    Conference

    ConferenceICASSP´98, IEEE 1998 Int.Conf. on Acoustics, Speech, and Signal Processing
    CitySeattle, USA
    Period01/01/1998 → …

    Bibliographical note

    Copyright: 1998 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

    Fingerprint Dive into the research topics of 'Adaptive Regularization of Neural Networks Using Conjugate Gradient'. Together they form a unique fingerprint.

    Cite this