Sequential non-Gaussian pattern recognition with supervised learning

This paper considers binary pattern recognition of a non-Gaussian pattern in Gaussian noise using supervised learning. The scheme is both structure and parameter adaptive. To facilitate a feasible solution, certain judicious approximations are used. Two examples are presented to demonstrate the learning capability of the proposed algorithms.