Neural classifier construction using regularization, pruning and test error estimation.
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.
Hintz-Madsen
Mads
M
CONNECT, Department of Mathematical Modelling, Building 321, Technical University of Denmark, DK-2800, Lyngby, Denmark
Kai Hansen
Lars
L
Larsen
Jan
J
With Pedersen
Morten
M
Larsen
Michael
M