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https://hdl.handle.net/20.500.14279/1626
Πεδίο DC | Τιμή | Γλώσσα |
---|---|---|
dc.contributor.author | Demiris, Yiannis | - |
dc.contributor.author | Chatzis, Sotirios P. | - |
dc.date.accessioned | 2013-02-19T15:22:49Z | en |
dc.date.accessioned | 2013-05-17T05:22:24Z | - |
dc.date.accessioned | 2015-12-02T10:02:17Z | - |
dc.date.available | 2013-02-19T15:22:49Z | en |
dc.date.available | 2013-05-17T05:22:24Z | - |
dc.date.available | 2015-12-02T10:02:17Z | - |
dc.date.issued | 2011-09 | - |
dc.identifier.citation | IEEE transactions on neural networks, 2011, vol. 22, no. 9, pp. 1435-1445 | en_US |
dc.identifier.issn | 10459227 | - |
dc.identifier.uri | https://hdl.handle.net/20.500.14279/1626 | - |
dc.description.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 | en_US |
dc.format | en_US | |
dc.language.iso | en | en_US |
dc.relation.ispartof | IEEE transactions on neural networks and learning systems | en_US |
dc.rights | © IEEE | en_US |
dc.subject | Bayesian inference | en_US |
dc.subject | Reservoir computing | en_US |
dc.subject | Gaussian processes | en_US |
dc.subject | Sequential data modeling | en_US |
dc.title | Echo state Gaussian process | en_US |
dc.type | Article | en_US |
dc.collaboration | Imperial College London | en_US |
dc.subject.category | Electrical Engineering - Electronic Engineering - Information Engineering | en_US |
dc.journals | Subscription | en_US |
dc.country | United Kingdom | en_US |
dc.subject.field | Engineering and Technology | en_US |
dc.publication | Peer Reviewed | en_US |
dc.identifier.doi | 10.1109/TNN.2011.2162109 | en_US |
dc.dept.handle | 123456789/54 | en |
dc.relation.issue | 9 | en_US |
dc.relation.volume | 22 | en_US |
cut.common.academicyear | 2011-2012 | en_US |
dc.identifier.spage | 1435 | en_US |
dc.identifier.epage | 1445 | en_US |
item.fulltext | No Fulltext | - |
item.languageiso639-1 | en | - |
item.grantfulltext | none | - |
item.openairecristype | http://purl.org/coar/resource_type/c_6501 | - |
item.cerifentitytype | Publications | - |
item.openairetype | article | - |
crisitem.journal.journalissn | 2162237X | - |
crisitem.journal.publisher | IEEE | - |
crisitem.author.dept | Department of Electrical Engineering, Computer Engineering and Informatics | - |
crisitem.author.faculty | Faculty of Engineering and Technology | - |
crisitem.author.orcid | 0000-0002-4956-4013 | - |
crisitem.author.parentorg | Faculty of Engineering and Technology | - |
Εμφανίζεται στις συλλογές: | Άρθρα/Articles |
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