An error-entropy minimization algorithm for supervised training of nonlinear adaptive systems

An information-theoretic supervised learning criterion for adaptive systems, minimum error entropy (MEE), is discussed. Equivalence is demonstrated between minimization of error's Renyi entropy of order omega and minimization of a Csiszar distance measure between the densities of desired system outputs.