Abstract
This paper demonstrates the inherent capacity of the ensemble learning approach to stabilize the notoriously difficult to tune echo state network (ESN) learning machines. ESN learning machines belong to a class of recurrent neural networks made popular because of their ability to perform predictively well on time series data. However, for any given task, building the corresponding optimal ESN is notoriously hard because of the instability of the tuning process. In this paper, we harness the high predictive variance by building ensembles made up of lightly tuned ESNs, and then combining those ESNs (base learners) rather than seeking the daunting task of selecting a single one. Throughout this paper, all our examples consistently demonstrate substantial reductions in average prediction error as a result of using the aggregate learning machine in place of the single one.
Qiuyi Wu
Postdoc Research Fellow
My research interests include functional data analysis, image processing and high dimensional data anlaysis.