Mapping multi-layer attributed graphs onto recognition network

Abstract

A methodology of synthesizing a neocognitron is presented. The goal is that the system parameters is a neocognitron can be 'programmed' rather than learned through laborious training. The tool used is the attribute graph theory. Using a set of attribute graphs describing structural and contextual information of different classes of patterns, one can synthesize a neocognitron through a mapping algorithm. The deformation-invariant attribute of the neocognitron can be preserved through the blurring of S-cells. The performance of the neocognitron obtained through the synthesis is contrasted with that of an identical neocognitron obtained through supervised training.<>