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Τίτλος: A Reservoir-driven non-stationary hidden markov model
Συγγραφείς: Demiris, Yiannis 
Chatzis, Sotirios P. 
metadata.dc.contributor.other: Χατζής, Σωτήριος Π.
Δεμίρης, Γιάννης
Major Field of Science: Engineering and Technology
Field Category: Electrical Engineering - Electronic Engineering - Information Engineering
Λέξεις-κλειδιά: Pattern recognition;Computer science;Markov processes;Dirichlet process;Reservoir;Hidden Markov model
Ημερομηνία Έκδοσης: Μαΐ-2012
Πηγή: Pattern recognition, 2012, vol. 45, no. 11, pp. 3985–3996
Volume: 45
Issue: 11
Start page: 3985
End page: 3996
Περιοδικό: Pattern recognition 
Περίληψη: 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
URI: https://hdl.handle.net/20.500.14279/4146
ISSN: 00313203
DOI: 10.1016/j.patcog.2012.04.018
Rights: © 2012 Elsevier. All rights reserved.
Attribution-NonCommercial-NoDerivs 3.0 United States
Type: Article
Affiliation: Cyprus University of Technology 
Imperial College London 
Publication Type: Peer Reviewed
Εμφανίζεται στις συλλογές:Άρθρα/Articles

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