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The Extreme Learning Machine (ELM) is a single-hidden layer feedforwardneural network (SLFN) learning algorithm that can learn effectively andquickly. The ELM training phase assigns the input weights and bias randomly anddoes not change them in the whole process. Although the network works well, therandom weights in the input layer can make the algorithm less effective andimpact on its performance. Therefore, we propose a new approach to determinethe 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-knownapproach to improve the ELM and a state of the art algorithm to select theweights for the ELM. The results show that the RBM-ELM outperforms bothmethodologies and achieve a better performance than the ELM.

Restricted Boltzmann machine to determine the input weights for extreme learning machines