Learning from examples with quadratic mutual information
Discusses an algorithm to train nonlinear mappers with information theoretic criteria (entropy or mutual information) directly from a training set. The method is based on a Parzen window estimator and uses Renyi's quadratic definition of entropy and a distance measure based on the Cauchy-Schwartz inequality. We apply the algorithm to the difficult problem of vehicle pose estimation in synthetic aperture radar (SAR) with very good results.