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D. Pados and P. Papantoni-Kazakos, “A Class of Neyman-Pearson and Bayes Learning Algorithms for Neural Classification,” IEEE International Symposium on Information Theory, Trondheim, 1-27 July 1994.

has been cited by the following article:

• TITLE: Stochastic Binary Neural Networks for Qualitatively Robust Predictive Model Mapping

  AUTHORS: A. T. Burrell, P. Papantoni-Kazakos

  KEYWORDS: Qualitative Robustness; Predictive Model Mapping; Stochastic Approximation; Stochastic Binary Neural Networks; Real-Time Supervised Learning; Ergodicity

  JOURNAL NAME: International Journal of Communications, Network and System Sciences, Vol.5 No.9A, September 18, 2012

  ABSTRACT: We consider qualitatively robust predictive mappings of stochastic environmental models, where protection against outlier data is incorporated. We utilize digital representations of the models and deploy stochastic binary neural networks that are pre-trained to produce such mappings. The pre-training is implemented by a back propagating supervised learning algorithm which converges almost surely to the probabilities induced by the environment, under general ergodicity conditions.