Linear Separability of Signal Space as a Basis for Adaptive Mechanisms

Abstract

This report reviews the research results of the program entitled “Linear Separability of Signal Space as a Basis for Adaptive Mechanisms”. The major contributions of this program have been two fold: 1) the notion of discriminant functions for organizing past and present knowledge into a basis for further theoretical research on trainable pattern classifying machines, and 2) some significant new results have been obtained on trainable pattern classifying machines.