Publication details

Recurrent Bayesian Reasoning in Probabilistic Neural Networks

Conference Paper (international conference)

Grim Jiří, Hora Jan

serial: Artificial Neural Networks - ICANN 2007, p. 129-138 , Eds: Marques de Sá J., Alexandre L. A., Duch W., Mandic D.

action: International Conference on Artificial Neural Networks /17./, (Porto, PT, 09.09.2007-13.09.2007)

research: CEZ:AV0Z10750506

project(s): 507752, , 1M0572, GA MŠk, 2C06019, GA MŠk, GA102/07/1594, GA ČR

keywords: neural networks, probabilistic approach, distribution mixtures

abstract (eng):

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.

abstract (cze):

Rekurentní bayesovské odvozování v pravděpodobnostních neuronových sítích