Genetic algorithms and classifier systems: foundations and future directions
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
The transformative compression of coherent languages
The transformative compression of coherent languages
Training multilayer perceptrons with the extended Kalman algorithm
Advances in neural information processing systems 1
Neural network recognizer for hand-written zip code digits
Advances in neural information processing systems 1
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations
Machine Learning
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One of the principal inefficiencies in the multicategory perceptron algorithm lies in its “training algorithm”. This problem has been dealt with in the past by having multiple perceptrons trained to respond to different predefined features in the input vector using back propagation. The problem with this approach is first that in general, one cannot be sure that an appropriate set of feature vectors has been defined and second, even if it were possible to do so, one cannot insure their relative spatial geometries. A new approach for reducing the dimensionality of the pattern vector utilizes the associative memory which emerges from the cooperation among multiple distributed multicategory perceptrons connected by a genetic algorithm.