Neural Classifier Construction using Regularization, Pruning

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

    Research output: Contribution to journalJournal articleResearchpeer-review

    Abstract

    In this paper we propose a method for construction of feed-forward neural classifiers based on regularization and adaptive architectures. Using a penalized maximum likelihood scheme, we derive a modified form of the entropic error measure and an algebraic estimate of the test error. In conjunction with optimal brain damage pruning, a test error estimate is used to select the network architecture. The scheme is evaluated on four classification problems.
    Original languageEnglish
    JournalNeural Networks
    Volume11
    Issue number9
    Pages (from-to)1659-1670
    ISSN0893-6080
    Publication statusPublished - 1998

    Fingerprint Dive into the research topics of 'Neural Classifier Construction using Regularization, Pruning'. Together they form a unique fingerprint.

    Cite this