LAPACK Users' guide (third ed.)
LAPACK Users' guide (third ed.)
Benchmarking reservoir computing on time-independent classification tasks
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Operational support in fish farming through case-based reasoning
IEA/AIE'12 Proceedings of the 25th international conference on Industrial Engineering and Other Applications of Applied Intelligent Systems: advanced research in applied artificial intelligence
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Reservoir computing is a recent paradigm that has proved to be quite effective given the classical difficulty in training recurrent neural networks. An approach to using reservoir recurrent neural networks has been recently proposed for static problems and in this paper we look at the influence of the reservoir size, spectral radius and connectivity on the classification error in these problems. The main conclusion derived from the performed experiments is that only the size of the reservoir is relevant with the spectral radius and the connectivity of the reservoir not affecting the classification performance.