Optimization and application of a RAM-based neural network for fast image processing tasks
Thomas M. Joergensen, Steen S. Christensen, Allan W. Andersen, Christian Liisberg · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1994
A RAM-based neural network applicable for object detection in machine vision is considered. It is shown that it is easy to perform a crossvalidation test for the training set using this network type. This is relevant for measuring the network generalization capability (robustness). An information measure combining the concept of crossvalidation and Shannon information is proposed. We describe how this measure can be used to select the input connections of the network. The task of recognizing handwritten digits is used to demonstrate the capability of the selection strategy.