Title :
Design and evaluation of neural classifiers
Author :
Hintz-Madsen, Mads ; Pedersen, Morten With ; Hansen, Lars Kai ; Larsen, Jan
Author_Institution :
Inst. of Math. Modeling, Tech. Univ., Lyngby, Denmark
Abstract :
In this paper we propose a method for the design of feedforward neural classifiers based on regularization and adaptive architectures. Using a penalized maximum likelihood scheme we derive a modified form of the entropy error measure and an algebraic estimate of the test error. In conjunction with optimal brain damage pruning the test error estimate is used to optimize the network architecture. The scheme is evaluated on an artificial and a real world problem
Keywords :
adaptive systems; entropy; error analysis; feedforward neural nets; maximum likelihood estimation; neural net architecture; optimisation; pattern classification; adaptive architectures; algebraic error estimate; entropy error measure; feedforward neural networks; glass classification; neural classifiers; optimal brain damage pruning; pattern classification; penalized maximum likelihood estimation; Artificial neural networks; Biological neural networks; Computer architecture; Feedforward systems; Frequency estimation; Maximum likelihood estimation; Optimization methods; Pattern recognition; Probability distribution; Testing;
Conference_Titel :
Neural Networks for Signal Processing [1996] VI. Proceedings of the 1996 IEEE Signal Processing Society Workshop
Conference_Location :
Kyoto
Print_ISBN :
0-7803-3550-3
DOI :
10.1109/NNSP.1996.548352
