An Echo State Network transforms an incoming time series signal into a high-dimensional state space, and, of course, not every dimension may contribute to the solution. We argue that giving low weights via linear regression is not sufficient. Instead irrelevant features should be entirely excluded from directly contributing to the output nodes. We conducted several experiments using two state-of-the-art feature selection algorithms. Results show significant reduction of the generalization error. © 2010 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Kobialka, H. U., & Kayani, U. (2010). Echo state networks with sparse output connections. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6352 LNCS, pp. 356–361). https://doi.org/10.1007/978-3-642-15819-3_47
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