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Neural classifier construction using regularization, pruning and test error estimation
Authors:Mads Hintz-Madsen  Lars Kai Hansen  Jan Larsen  Morten With Pedersen  Michael Larsen
Institution:

CONNECT, Department of Mathematical Modelling, Building 321, Technical University of Denmark, DK-2800 Lyngby, Denmark

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.
Keywords:artificial neural network  classification  analytical error
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