Adaptive Regularization of Neural Networks Using Conjugate Gradient

Cyril Goutte, Jan Larsen

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    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
    Place of PublicationNew York
    Publication date1998
    ISBN (Print)0-7803-4428-6
    Publication statusPublished - 1998
    EventICASSP´98, IEEE 1998 Int.Conf. on Acoustics, Speech, and Signal Processing - Seattle, USA
    Duration: 1 Jan 1998 → …


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

    Bibliographical note

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