Introduction to the theory of neural computation
Introduction to the theory of neural computation
Maximum-Likelihood Design of Layered Neural Networks
ICPR '96 Proceedings of the International Conference on Pattern Recognition (ICPR '96) Volume IV-Volume 7472 - Volume 7472
Extraction of Binary Features by Probabilistic Neural Networks
ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part II
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Considering the probabilistic approach to neural networks in the framework of statistical pattern recognition we assume approximation of class-conditional probability distributions by finite mixtures of product components. The mixture components can be interpreted as probabilistic neurons in neurophysiological terms and, in this respect, the fixed probabilistic description becomes conflicting with the well known short-term dynamic properties of biological neurons. We show that some parameters of PNN can be "released" for the sake of dynamic processes without destroying the statistically correct decision making. In particular, we can iteratively adapt the mixture component weights or modify the input pattern in order to facilitate the correct recognition.