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In order to circumvent the weakness of very slow convergence of most traditional learning algorithms for single layer feedforward neural networks, the extreme learning machines (ELM) has been recently developed to achieve extremely fast learning with good performance by training only for the output weights. However, it cannot be applied to multiple-hidden layer feedforward neural networks (MLFN),...
Feedforward neural networks have been extensively used to approximate complex nonlinear mappings directly from the input samples. However, their traditional learning algorithms are usually much slower than required. In this work, two hidden-feature-space ridge regression methods HFSR and centered-ELM are first proposed for feedforward networks. As the special kernel methods, the important characteristics...
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