Unsupervised learning pattern recognition

This paper constitutes Part II of a series of papers on adaptive pattern recognition and its applications. It pertains to optimal, unsupervised learning, adaptive pattern recognition of "lumped" gaussian signals in white gaussian noise. Specifically, both deterministic decision directed learning as well as random decision directed learning algorithms for continuous data are obtained. It is shown that the supervised learning results [1], in particular the partition theorem are applicable in the directed learning approach to the unsupervised case [2].