Extreme Learning Machine (ELM) is a new paradigm for using Single-hidden Layer Feedforward Networks (SLFNs) with a much simpler training method. The input weights and the bias of the hidden layer are randomly chosen and output weights are analytically determined. One of the open problems in ELM research is how to automatically determine network architectures for given tasks. In this paper, it is taken as a model selection problem, a modified fast recursive algorithm (MFRA) is introduced to quickly and efficiently estimate the contribution of each hidden layer node to the decrease of the net function, and then a leave one out (LOO) cross validation is used to select the optimal number of hidden layer nodes. Simulation results on both artificial and real world benchmark datasets indicate the effectiveness of the proposed method.
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