Design and evaluation of neural classifiers

Mads Hintz-Madsen, Morten With Pedersen, Lars Kai Hansen, Jan Larsen

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    Abstract

    In this paper we propose a method for the design of feedforward neural classifiers based on regularization and adaptive architectures. Using a penalized maximum likelihood scheme we derive a modified form of the entropy error measure and an algebraic estimate of the test error. In conjunction with optimal brain damage pruning the test error estimate is used to optimize the network architecture. The scheme is evaluated on an artificial and a real world problem
    Original languageEnglish
    Title of host publicationProceedings of the IEEE Signal Processing Society Workshop Neural Networks for Signal Processing
    PublisherIEEE
    Publication date1996
    Pages223-232
    ISBN (Print)07-80-33550-3
    DOIs
    Publication statusPublished - 1996
    EventNeural Network for Signal Processing - Kyoto
    Duration: 1 Jan 1996 → …
    Conference number: 6th

    Conference

    ConferenceNeural Network for Signal Processing
    Number6th
    CityKyoto
    Period01/01/1996 → …

    Bibliographical note

    Copyright 1996 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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