TITLE: Neural Classifier Construction Using Regularization, Pruning
and Test Error Estimation
AUTHOR: Mads Hintz-Madsen, Lars Kai Hansen, Jan Larsen,
Morten With Pedersen, and Michael Larsen
connect, Department of Mathematical Modelling, Building 321
Technical University of Denmark, DK-2800 Lyngby, Denmark
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.
To appear in Neural Networks, 1998.