TITLE: Optimized Combination, Regularization, and Pruning in Parallel Consensual Neural Networks
AUTHOR: J.A. Benediktssona), J. Larsenb),
J.R. Svienssona) & L.K. Hansenb)
Department of Electrical and Computer Engineering
University of Iceland
Hjardarhaga 2-6, 107 Reyjkavik, Iceland
connect, Department of Mathematical Modelling, Building 321
Technical University of Denmark, DK-2800 Lyngby, Denmark
Optimized combination, regularization, and pruning is
proposed for the Parallel Consensual Neural Networks (PCNNs) which
is a neural network architecture based on the consensus of a collection of stage neural
the same input data with different representations.
Here, a regularization scheme is presented for the PCNN which iteratively
adapts the regularization parameters by minimizing the validation
error. The use of this adaptive regularization scheme in
conjunction with Optimal Brain Damage pruning is suggested both to optimize the
architecture of the individual stage networks and to avoid overfitting.
Experiments are conducted on a multisource remote sensing and geographic
data set consisting of six data source. The results obtained by the proposed
version of PCNN are compared to other classification
approaches such as the original PCNN,
single stage neural networks and statistical classifiers.
In comparison to the originally proposed PCNNs,
the use of pruning and regularization not only produces
simpler PCNNs but
also gives higher classification accuracies.
In particular, a neural network based non-linear combination
scheme for the individual stages in the PCNN
produces excellent overall classification accuracies for both training
In Proceedings of European Symposium on Remote Sensing, vol. 3500,
Barcelona, Spain, 21-25, Sept. 1998.