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


    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
    Issue number9
    Pages (from-to)1659-1670
    Publication statusPublished - 1998


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