A Method of Regularization for the BESOM Bayesian Network

Abstract

We describe a method of regularization for the restricted Bayesian network BESOM, which has network structure like deep learning. Two types of priors, win-rate penalty and lateral inhibition penalty are introduced to avoid overfitting and local minimum problems.