Παρακαλώ χρησιμοποιήστε αυτό το αναγνωριστικό για να παραπέμψετε ή να δημιουργήσετε σύνδεσμο προς αυτό το τεκμήριο:
https://hdl.handle.net/20.500.14279/1626
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 |
Εμφανίζεται στις συλλογές: | Άρθρα/Articles |
CORE Recommender
SCOPUSTM
Citations
103
checked on 9 Νοε 2023
WEB OF SCIENCETM
Citations
5
84
Last Week
0
0
Last month
2
2
checked on 29 Οκτ 2023
Page view(s)
539
Last Week
0
0
Last month
26
26
checked on 13 Μαρ 2025
Google ScholarTM
Check
Altmetric
Όλα τα τεκμήρια του δικτυακού τόπου προστατεύονται από πνευματικά δικαιώματα