Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.14279/4146
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Demiris, Yiannis | - |
dc.contributor.author | Chatzis, Sotirios P. | - |
dc.contributor.other | Χατζής, Σωτήριος Π. | - |
dc.contributor.other | Δεμίρης, Γιάννης | - |
dc.date.accessioned | 2013-02-19T15:46:00Z | en |
dc.date.accessioned | 2013-05-17T10:30:09Z | - |
dc.date.accessioned | 2015-12-09T11:30:40Z | - |
dc.date.available | 2013-02-19T15:46:00Z | en |
dc.date.available | 2013-05-17T10:30:09Z | - |
dc.date.available | 2015-12-09T11:30:40Z | - |
dc.date.issued | 2012-05 | - |
dc.identifier.citation | Pattern recognition, 2012, vol. 45, no. 11, pp. 3985–3996 | en_US |
dc.identifier.issn | 00313203 | - |
dc.identifier.uri | https://hdl.handle.net/20.500.14279/4146 | - |
dc.description.abstract | In this work, we propose a novel approach towards sequential data modeling that leverages the strengths of hidden Markov models and echo-state networks (ESNs) in the context of non-parametric Bayesian inference approaches. We introduce a non-stationary hidden Markov model, the time-dependent state transition probabilities of which are driven by a high-dimensional signal that encodes the whole history of the modeled observations, namely the state vector of a postulated observations-driven ESN reservoir. We derive an efficient inference algorithm for our model under the variational Bayesian paradigm, and we examine the efficacy of our approach considering a number of sequential data modeling applications | en_US |
dc.format | en_US | |
dc.language.iso | en | en_US |
dc.relation.ispartof | Pattern recognition | en_US |
dc.rights | © 2012 Elsevier. All rights reserved. | * |
dc.rights | Attribution-NonCommercial-NoDerivs 3.0 United States | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/us/ | * |
dc.subject | Pattern recognition | en_US |
dc.subject | Computer science | en_US |
dc.subject | Markov processes | en_US |
dc.subject | Dirichlet process | en_US |
dc.subject | Reservoir | en_US |
dc.subject | Hidden Markov model | en_US |
dc.title | A Reservoir-driven non-stationary hidden markov model | en_US |
dc.type | Article | en_US |
dc.collaboration | Cyprus University of Technology | en_US |
dc.collaboration | Imperial College London | en_US |
dc.subject.category | Electrical Engineering - Electronic Engineering - Information Engineering | en_US |
dc.journals | Hybrid Open Access | en_US |
dc.review | peer reviewed | - |
dc.country | Cyprus | 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 | 10.1016/j.patcog.2012.04.018 | en_US |
dc.dept.handle | 123456789/134 | en |
dc.relation.issue | 11 | en_US |
dc.relation.volume | 45 | en_US |
cut.common.academicyear | 2011-2012 | en_US |
dc.identifier.spage | 3985 | en_US |
dc.identifier.epage | 3996 | en_US |
item.grantfulltext | none | - |
item.openairecristype | http://purl.org/coar/resource_type/c_6501 | - |
item.fulltext | No Fulltext | - |
item.languageiso639-1 | en | - |
item.cerifentitytype | Publications | - |
item.openairetype | article | - |
crisitem.journal.journalissn | 0031-3203 | - |
crisitem.journal.publisher | Elsevier | - |
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 | - |
Appears in Collections: | Άρθρα/Articles |
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