Hyperparameter Selection for Self-Organizing Maps
نویسندگان
چکیده
منابع مشابه
Hyperparameter Selection for Self-Organizing Maps
The self-organizing map (SOM) algorithm for finite data is derived as an approximate MAP estimation algorithm for a Gaussian mixture model with a Gaussian smoothing prior, which is equivalent to a generalized deformable model (GDM). For this model, objective criteria for selecting hyperparameters are obtained on the basis of empirical Bayesian estimation and crossvalidation, which are represent...
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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 obtaine...
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Self-Organizing Visual Maps
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ژورنال
عنوان ژورنال: Neural Computation
سال: 1997
ISSN: 0899-7667,1530-888X
DOI: 10.1162/neco.1997.9.3.623