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Practical Considerations in Training Extreme Learning Machines

Extreme learning machines (ELM) represent a new fast learning algorithm for single layer feedforward networks. In this paper, we investigate several practical properties of training ELM in the context of a simulated function approximation task. ELM with different hidden layer activation functions and varying number of hidden nodes are applied in the learning task and the function approximation accuracy is examined with respect to the range scaling of input variables. The results demonstrate that ELM models are very sensitive with respect to proper input scaling. The approximate range of optimal ELM performance covers only input range scaling within an order of magnitude. Comparison with classical feedforward neural networks with sigmoidal activation functions shows that these are not affected by input range scaling. Results encourage further studies of practical aspect of efficient training and applying ELM.