Incremental extreme learning machine
This new theory shows that in order to let SLFNs work as universal approximators, one may simply randomly choose input-to-hidden nodes, and then we only need to adjust the output weights linking the hidden layer and the output layer. In such SLFNs implementations, the activation functions for additi...
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| Format: | Thesis |
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2008
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| Online Access: | https://hdl.handle.net/10356/3804 |