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|Title:||Echo state Gaussian process||Authors:||Demiris, Yiannis
Chatzis, Sotirios P.
|Keywords:||Communication, Networking & Broadcasting
Computing & Processing (Hardware/Software)
|Issue Date:||2011||Publisher:||IEEE Xplore||Source:||IEEE transactions on neural networks, 2011, Volume 22, Issue 9, Pages 1435-1445||Abstract:||Echo state networks (ESNs) constitute a novel approach to recurrent neural network (RNN) training, with an RNN (the reservoir) being generated randomly, and only a readout being trained using a simple computationally efficient algorithm. ESNs have greatly facilitated the practical application of RNNs, outperforming classical approaches on a number of benchmark tasks. In this paper, we introduce a novel Bayesian approach toward ESNs, the echo state Gaussian process (ESGP). The ESGP combines the merits of ESNs and Gaussian processes to provide a more robust alternative to conventional reservoir computing networks while also offering a measure of confidence on the generated predictions (in the form of a predictive distribution). We exhibit the merits of our approach in a number of applications, considering both benchmark datasets and real-world applications, where we show that our method offers a significant enhancement in the dynamical data modeling capabilities of ESNs. Additionally, we also show that our method is orders of magnitude more computationally efficient compared to existing Gaussian process-based methods for dynamical data modeling, without compromises in the obtained predictive performance||URI:||http://ktisis.cut.ac.cy/handle/10488/7224||ISSN:||1045-9227||DOI:||10.1109/TNN.2011.2162109||Rights:||© 2006 IEEE|
|Appears in Collections:||Άρθρα/Articles|
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