Titlebook: Artificial Neural Networks - ICANN 2007; 17th International C Joaquim Marques Sá,Luís A. Alexandre,Danilo Mandic Conference proceedings 200

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https://doi.org/10.1007/978-3-540-74690-4Boolean function; algorithmic learning; algorithms; bioinspired computing; biomedical data analysis; clas

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978-3-540-74689-8Springer-Verlag Berlin Heidelberg 2007

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Gang der Versuchsdurchrechnungen,al to achieve their greater prediction ability. A standard training of these neural networks uses pseudoinverse matrix for one-step learning of weights from hidden to output neurons. This regular adaptation of Echo State neural networks was optimized by updating the weights of the dynamic reservoir

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Versuche mit gasreichen Kohlen,ds on the size of neural networks that are unrealistic to implement. This work provides a computational study for estimating the size of neural networks using as an estimation parameter the size of available training data. We will also show that the size of a neural network is problem dependent and