Restricted Boltzmann Machine with Multivalued Hidden Variables: a model suppressing over-fitting

Generalization is one of the most important issues in machine learning problems. In this paper, we consider the generalization in restricted Boltzmann machines. We propose a restricted Boltzmann machine with multivalued hidden variables, which is a simple extension of conventional restricted Boltzmann machines. We demonstrate that our model is better than the conventional one via numerical experiments: experiments for a contrastive divergence learning with artificial data and for a classification problem with MNIST.