Kobialka, Hans-UlrichHans-UlrichKobialkaKayani, U.U.Kayani2022-03-112022-03-112010https://publica.fraunhofer.de/handle/publica/36700010.1007/978-3-642-15819-3_47An 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.enecho state networkfeature selection005006629400conference paper