A probabilistic self-organizing classification neural network architecture

This paper introduces a new analysis of the output map of Kohonen´s self-organizing map. Using this analysis we are able to use the SOM as a supervised net. PSSOM´s major advantage is its ability to assign degrees of classification certainty to unseen test data. This paper also investigates the application of this analysis as a first level in a hybrid neural network model. Our experiments show how this analysis tool can be used at the root of a hierarchical classifier model to increase considerably the overall speed of network training without loss of accuracy