Training SOM with adaptative neighborhood radius in a variational Bayesian framework

A Bayesian Variational principle for dynamic Self Organizing Maps

We propose organisation conditions that yield a method for training the self-similar model (som) with adaptative neighborhood radius in a variational bayesian framework.This method is validated on a non-stationary setting and compared in an high-dimensionalsetting with an other adaptative method.Self-similar model (ssm) is a generalisation of the self-similar model (ssm), which has been widely used as a model for self-similarity (ssm).Ssm is a generalization of the self-similar model (ssm) that has been widely used as a model for self-similarity (ssm).Ssm is a generalization of the self-similar model (ssm), which has been widely used as a model for self-similarity (ssm).Ssm is a generalization of the self-similar model (ssm), which has been widely used as a model for self-similarity (ssm).Ssm has been widely used as a model for self-similarity (scc), which is a generalization of the self-similar model (scc) @xcite.Scc is a generalization of the self-similar model (scc) @xcite.Scc is a generalization of the self-similar model (scc) @xcite.Scc is a generalization of the self-similar model (scc) @xcite.Scc is a generalization of the self-similar model (scc) @xcite.