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Energy, entropy, and information potential for neural computation

Energy, entropy, and information potential for neural computation

The major goal of this research is to develop general nonparametric methods for the estimation of entropy and mutual information, giving a unifying point of view for their use in signal processing and neural computation. In many real world problems, the information is carried solely by data samples without any other a priori knowledge. The central issue of "learning from examples" is to estimate energy, entropy or mutual information of a variable only from its samples and adapt the system parameters by optimizing a criterion based on the estimation.
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