Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Mutual Information in Learning Feature Transformations
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Entropy minimization for supervised digital communications channelequalization
IEEE Transactions on Signal Processing
Generalized information potential criterion for adaptive system training
IEEE Transactions on Neural Networks
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Adaptive systems research is mainly concentrated around optimizing cost functions suitable to problems. Recently, Principe et al. proposed a particle interaction model for information theoretical learning. In this paper, inspired by this idea, we propose a generalization to the particle interaction model for learning and system adaptation. In addition, for the special case of supervised multi-layer perceptron (MLP) training we propose the interaction force backpropagation algorithm, which is a generalization of the standard error backpropagation algorithm for MLPs.