  Boltzmann machine, as a fundamental construction block of deep belief network
and deep Boltzmann machines, is widely used in deep learning community and
great success has been achieved. However, theoretical understanding of many
aspects of it is still far from clear. In this paper, we studied the Rademacher
complexity of both the asymptotic restricted Boltzmann machine and the
practical implementation with single-step contrastive divergence (CD-1)
procedure. Our results disclose the fact that practical implementation training
procedure indeed increased the Rademacher complexity of restricted Boltzmann
machines. A further research direction might be the investigation of the VC
dimension of a compositional function used in the CD-1 procedure.
