An Empirical Study of the L2-Boost technique with Echo State Networks

Authors

  • Sebastián Basterrech National Supercomputing Center VSB-Technical University of Ostrava

Keywords:

Boosting, L2-boosting, Echo State Network, Timeseries modeling, Reservoir Computing, Ensemble Methods

Abstract

A particular case of Recurrent Neural Network (RNN) was introduced at the beginning of the 2000s under the name of Echo State Networks (ESNs). The ESN model overcomes the limitations during the training of the RNNs while introducing no significant disadvantages. Although the model presents some well-identified drawbacks when the parameters are not well initialized. The performance of an ESN is highly dependent on its internal parameters and pattern of connectivity of the hiddenhidden weights Often, the tuning of the network parameters can be hard and can impact in the accuracy of the models. In this work, we investigate the performance of a specific boosting technique (called L2-Boost) with ESNs as single predictors. The L2-Boost technique has been shown to be an effective tool to combine “weak” predictors in regression problems. In this study, we use an ensemble of random initialized ESNs (without control their parameters) as “weak” predictors of the boosting procedure. We evaluate our approach on five well-know
time-series benchmark problems. Additionally, we comparethis technique with a baseline approach that consists of averaging the prediction of an ensemble of ESNs.

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Published

2014-04-01

How to Cite

Sebastián Basterrech. (2014). An Empirical Study of the L2-Boost technique with Echo State Networks. Journal of Network and Innovative Computing, 2, 8. Retrieved from https://cspub-jnic.org/index.php/jnic/article/view/57

Issue

Vol. 2 (2014)

Section

Original Article

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