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dc.contributor.authorDemiris, Yiannis-
dc.contributor.authorChatzis, Sotirios P.-
dc.date.accessioned2013-02-19T15:22:49Zen
dc.date.accessioned2013-05-17T05:22:24Z-
dc.date.accessioned2015-12-02T10:02:17Z-
dc.date.available2013-02-19T15:22:49Zen
dc.date.available2013-05-17T05:22:24Z-
dc.date.available2015-12-02T10:02:17Z-
dc.date.issued2011-09-
dc.identifier.citationIEEE transactions on neural networks, 2011, vol. 22, no. 9, pp. 1435-1445en_US
dc.identifier.issn10459227-
dc.identifier.urihttps://hdl.handle.net/20.500.14279/1626-
dc.description.abstractEcho 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 performanceen_US
dc.formatpdfen_US
dc.language.isoenen_US
dc.relation.ispartofIEEE transactions on neural networks and learning systemsen_US
dc.rights© IEEEen_US
dc.subjectBayesian inferenceen_US
dc.subjectReservoir computingen_US
dc.subjectGaussian processesen_US
dc.subjectSequential data modelingen_US
dc.titleEcho state Gaussian processen_US
dc.typeArticleen_US
dc.collaborationImperial College Londonen_US
dc.subject.categoryElectrical Engineering - Electronic Engineering - Information Engineeringen_US
dc.journalsSubscriptionen_US
dc.countryUnited Kingdomen_US
dc.subject.fieldEngineering and Technologyen_US
dc.publicationPeer Revieweden_US
dc.identifier.doi10.1109/TNN.2011.2162109en_US
dc.dept.handle123456789/54en
dc.relation.issue9en_US
dc.relation.volume22en_US
cut.common.academicyear2011-2012en_US
dc.identifier.spage1435en_US
dc.identifier.epage1445en_US
item.fulltextNo Fulltext-
item.languageiso639-1en-
item.grantfulltextnone-
item.openairecristypehttp://purl.org/coar/resource_type/c_6501-
item.cerifentitytypePublications-
item.openairetypearticle-
crisitem.journal.journalissn2162237X-
crisitem.journal.publisherIEEE-
crisitem.author.deptDepartment of Electrical Engineering, Computer Engineering and Informatics-
crisitem.author.facultyFaculty of Engineering and Technology-
crisitem.author.orcid0000-0002-4956-4013-
crisitem.author.parentorgFaculty of Engineering and Technology-
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