Pattern classification by a Gibbsian Kohonen neural network with an application to Arabic character recognition
Neila Mezghani, Amar Mitiche · 2008
Recent studies have shown that the Gibbs density function can model complex patterns and that a constrained maximum entropy formulation affords a powerful means of estimating its parameters from pattern class data. The theory, developed in the context of learning a prior model of natural images, has been applied successfully to the synthesis of textures and shapes, and to pattern classification. The basic parameter estimation algorithm rests on gradient algorithm following the maximization under constraints of an entropy criterion. The purpose of this study is to investigate a Gibbsian Kohonen neural network, a Kohonen network which can learn these constrained maximum entropy Gibbs density parameters for pattern representation and classification. Experiments in classification of handwritten characters verify the validity and efficiency of the method.