A resource-allocating network for function interpolation
Neural Computation
Error minimized extreme learning machine with growth of hidden nodes and incremental learning
IEEE Transactions on Neural Networks
OP-ELM: optimally pruned extreme learning machine
IEEE Transactions on Neural Networks
Universal approximation using incremental constructive feedforward networks with random hidden nodes
IEEE Transactions on Neural Networks
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Extreme learning machines are single-hidden layer feed-forward neural networks, where the training is restricted to the output weights in order to achieve fast learning with good performance. The success of learning strongly depends on the random parameter initialization. To overcome the problem of unsuited initialization ranges, a novel and efficient pretraining method to adapt extreme learning machines task-specific is presented. The pretraining aims at desired output distributions of the hidden neurons. It leads to better performance and less dependence on the size of the hidden layer.