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. © 1973, IEEE. All rights reserved.