FAUST: A Vision-Based Neural Network Multi-Map Pattern Recognition Architecture.

摘要

A new architecture is presented for multi-map, self-organizing pattern recognition which allows concurrent massively parallel learning of features using different maps for each feature type. The method used is similar to the multi-map structures known to exist in the vertebrate sensory cortex. The learning used to update memory locations uses a feed-forward mechanism and is self-organizing. The architecture is described by the acronym FAUST (Feed-forward Association Using Symmetrical Triggering). As a demonstration of the effectiveness of FAUST, a character recognition program has been constructed on a massively parallel computer which can perform 99% accurate character recognition on medium-quality machine printed digits at a speed of 2.4 ms/digit, and 88% recognition on multiple-writer hand print with a 2.3% substitutional error rate.