Abstract
Reservoir Computing (RC) is a recent research area, in which a untrained recurrent network of nodes is used for the recognition of temporal patterns. Contrary to Recurrent Neural Networks (RNN), where the weights of the connections between the nodes are trained, only a linear output layer is trained. We will introduce three different time-scales and show that the performance and computational complexity are highly dependent on these time-scales. This is demonstrated on an isolated spoken digits task.
This research is partially funded by FWO Flanders project G.0317.05.
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Schrauwen, B., Defour, J., Verstraeten, D., Van Campenhout, J. (2007). The Introduction of Time-Scales in Reservoir Computing, Applied to Isolated Digits Recognition. In: de Sá, J.M., Alexandre, L.A., Duch, W., Mandic, D. (eds) Artificial Neural Networks – ICANN 2007. ICANN 2007. Lecture Notes in Computer Science, vol 4668. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74690-4_48
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DOI: https://doi.org/10.1007/978-3-540-74690-4_48
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