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Gradient based hyperparameter optimization in Echo State Networks
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DOI：10.1016/j.neunet.2019.02.001.png)
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Abstract
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Like most machine learning algorithms, Echo State Networks possess several hyperparameters that have to be carefully tuned for achieving best performance. For minimizing the error on a specific task, we present a gradient based optimization algorithm, for the input scaling, the spectral radius, the leaking rate, and the regularization parameter. (C) 2019 Elsevier Ltd. All rights reserved.
Keywords:
Echo State Network
Reservoir computing
Hyperparameters
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