  The Extreme Learning Machine (ELM) is a single-hidden layer feedforward
neural network (SLFN) learning algorithm that can learn effectively and
quickly. The ELM training phase assigns the input weights and bias randomly and
does not change them in the whole process. Although the network works well, the
random weights in the input layer can make the algorithm less effective and
impact on its performance. Therefore, we propose a new approach to determine
the input weights and bias for the ELM using the restricted Boltzmann machine
(RBM), which we call RBM-ELM. We compare our new approach with a well-known
approach to improve the ELM and a state of the art algorithm to select the
weights for the ELM. The results show that the RBM-ELM outperforms both
methodologies and achieve a better performance than the ELM.
