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