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A topology-selection method for self-organizing maps (SOMs) based on empirical Bayesian inference is presented. This method is natural extension of the hyperparameter-selection method presented earlier, in which the SOM algorithm is regarded as an estimation algorithm for a Gaussian mixture model with a Gaussian smoothing prior on the centroid parameters, and optimal hyperparameters are obtained by maximizing their evidence. In the present paper, comparisons between models with differentdoi:10.1162/neco.19184.108.40.2063 fatcat:mb45yh56lrf35ighsday4tylli