  Deep Belief Network (DBN) has a deep architecture that represents multiple
features of input patterns hierarchically with the pre-trained Restricted
Boltzmann Machines (RBM). A traditional RBM or DBN model cannot change its
network structure during the learning phase. Our proposed adaptive learning
method can discover the optimal number of hidden neurons and weights and/or
layers according to the input space. The model is an important method to take
account of the computational cost and the model stability. The regularities to
hold the sparse structure of network is considerable problem, since the
extraction of explicit knowledge from the trained network should be required.
In our previous research, we have developed the hybrid method of adaptive
structural learning method of RBM and Learning Forgetting method to the trained
RBM. In this paper, we propose the adaptive learning method of DBN that can
determine the optimal number of layers during the learning. We evaluated our
proposed model on some benchmark data sets.
