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Τίτλος: Echo state Gaussian process
Συγγραφείς: Demiris, Yiannis 
Chatzis, Sotirios P. 
Major Field of Science: Engineering and Technology
Field Category: Electrical Engineering - Electronic Engineering - Information Engineering
Λέξεις-κλειδιά: Bayesian inference;Reservoir computing;Gaussian processes;Sequential data modeling
Ημερομηνία Έκδοσης: Σεπ-2011
Πηγή: IEEE transactions on neural networks, 2011, vol. 22, no. 9, pp. 1435-1445
Volume: 22
Issue: 9
Start page: 1435
End page: 1445
Περιοδικό: IEEE transactions on neural networks and learning systems 
Περίληψη: 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: https://hdl.handle.net/20.500.14279/1626
ISSN: 10459227
DOI: 10.1109/TNN.2011.2162109
Rights: © IEEE
Type: Article
Affiliation: Imperial College London 
Publication Type: Peer Reviewed
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