Kohonen feature maps as a supervised learning machine
Hiroyuki Ichiki, Masafumi Hagiwara, Masaki Nakagawa · 2002
Kohonen feature maps as a supervised learning machine are proposed and discussed. The proposed models adopt supervised learning without modifying the basic learning algorithm. They behave as a supervised learning machine, which can learn input-output functions in addition to the characteristics of the conventional Kohonen feature maps. In the pattern recognition problems, the proposed models can structure the recognition system more simply than the conventional method, i.e., structuring a pattern recognition machine using a supervised learning machine after pre-processing by the Kohonen feature map. The proposed models do not distinguish the input vectors from the desired vectors because they regard them as the same kind of vectors. Several examples are simulated in order to compare with the conventional supervised learning machines. The results indicate the effectiveness of the proposed models.>