Boltzmann Learning of Parameters in Cellular Neural Networks
نویسنده
چکیده
We use Bayesian methods to design cellular neural networks for signal processing tasks and the Boltzmann Machine learning rule for parameter estimation. The learning rule can be used for models with uhidden" units, or for compietely unsupervised learning. The latter is exemplified by unsupervised adaptation of an image segmentation cellular network, in particular we apply the learning rule to adaptive segmentation of satellite imagery.
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