'ITERN CLASS DEGENERACY IN AN UNRESTRICfED STORAGE DENSITY MEMORY

The study of distributed memory systems has produced a number of models which work well in limited domains. However, until recently, the application of such systems to realworld problems has been difficult because of storage limitations, and their inherent architectural (and for serial simulation, computational) complexity. Recent development of memories with unrestricted storage capacity and economical feedforward architectures has opened the way to the application of such systems to complex pattern recognition problems. However, such problems are sometimes underspecified by the features which describe the environment, and thus a significant portion of the pattern environment is often non-separable. We will review current work on high density memory systems and their network implementations. We will discuss a general learning algorithm for such high density memories and review its application to separable point sets. Finally, we will introduce an extension of this method for learning the probability distributions of non-separable point sets.