Handwritten digits recognition by a supervised Kohonen-like learning algorithm

The authors describe the application of a supervised learning algorithm, based on Kohonen´s self-organizing feature maps, to pattern recognition. They adopt an idea previously used for semantic map organization and discuss its adaptation to pattern recognition. The basic motivation is to organize the map by the patterns and their association targets simultaneously. A by-product of this process is that the class labeling of neurons on the map emerges during the learning phase. The algorithm and results obtained for a handwritten zip code database are presented