Unsupervised learning in nongaussian pattern recognition

摘要

This paper considers unsupervised learning, structure and parameter adaptive binary pattern recognition when a nongaussian pattern is observed in gaussian noise. Different schemes, none of which require growing or infinite memory, are suggested. To facilitate feasible solutions, certain judicious approximations are made use of. Two examples are presented to demonstrate the learning capability of the proposed algorithms.