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