Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Structures of the Covariance Matrices in the Classifier Design
SSPR '98/SPR '98 Proceedings of the Joint IAPR International Workshops on Advances in Pattern Recognition
Towards a theory of early visual processing
Neural Computation
Backpropagation applied to handwritten zip code recognition
Neural Computation
Application of the Biologically Inspired Network for Electroencephalogram Analysis
Proceedings of the International Conference, 7th Fuzzy Days on Computational Intelligence, Theory and Applications
Multiple Classification Systems in the Context of Feature Extraction and Selection
MCS '02 Proceedings of the Third International Workshop on Multiple Classifier Systems
Hi-index | 0.00 |
The hypothesis is that in the lowest hidden layers of biological systems "local subnetworks" are smoothing an input signal. The smoothing accuracy may serve as a feature to feed the subsequent layers of the pattern classification network. The present paper suggests a multistage supervised and "unsupervised" training approach for design and training of multilayer feed-forward networks. Following to the methodology used in the statistical pattern recognition systems we split functionally the decision making process into two stages. In an initial stage, we smooth the input signal in a number of different ways and, in the second stage, we use the smoothing accuracy as a new feature to perform a final classification.